#Amazon BI tools

[vuelitics1]

Discover how the world’s top companies are leveraging Business Intelligence (BI) to stay ahead of the competition! In this video, we break down the strategies and tools used by giants like Google, Amazon, Apple, and more to optimize operations, enhance customer experience, and drive innovation. From real-time data analysis to predictive analytics, these companies are transforming the way business is done.

Whether you’re a business owner, a data enthusiast, or just curious about how big brands like Netflix and Tesla use BI to gain a competitive edge, this video is a must-watch. Learn how Business Intelligence tools like Tableau, Microsoft Power BI, and SAP BusinessObjects are being used to make smarter decisions, predict customer behavior, and streamline operations.

Visit Our Webiste: https://vuelitics.com/

[jcmarchi]

10 Best AI Form Generators (August 2024)

New Post has been published on https://thedigitalinsider.com/10-best-ai-form-generators-august-2024/

10 Best AI Form Generators (August 2024)

Efficient data collection and user engagement are crucial for businesses and organizations. Artificial Intelligence (AI) has disrupted the form-building process, offering innovative solutions that streamline creation, enhance user experience, and provide valuable insights.

This article explores the top AI form generators that are transforming how we design, deploy, and analyze online forms. From natural language form creation to advanced analytics and seamless integrations, these platforms leverage AI to make form building more accessible, efficient, and powerful than ever before. Whether you’re a small business owner, a marketing professional, or an enterprise-level organization, these AI-powered tools offer features that can significantly improve your data collection strategies and workflow automation.

Fillout is an innovative AI-powered form builder that simplifies the process of creating dynamic, interactive forms. By leveraging the power of artificial intelligence, Fillout enables users to generate forms quickly and effortlessly, catering to a wide range of needs without the hassle of manual design. With its user-friendly interface and advanced AI capabilities, Fillout streamlines form creation, making it an ideal solution for businesses and individuals looking to collect data efficiently.

One of the standout features of Fillout is its ability to create forms from simple prompts. Users can describe the form they want, and Fillout’s AI will generate a tailored form based on their requirements. The platform also offers a powerful no-code editor, allowing users to customize their AI-generated forms further, ensuring a perfect fit for their specific needs. Fillout’s AI technology continuously learns and improves, providing users with intelligent suggestions and optimizations to enhance their forms’ performance and user engagement.

Key Features:

Fillout uses AI to create forms based on user prompts, saving time and effort.

The platform’s AI suggests design improvements and optimizations to create engaging, high-converting forms.

It constantly learns and adapts, providing users with increasingly accurate and efficient form-building suggestions.

Integrates with popular third-party apps and platforms, ensuring a smooth workflow and easy data management.

Enables real-time collaboration, allowing teams to work together on form creation and leveraging AI to streamline the process

Visit Fillout →

Jotform is a cutting-edge online form builder that also uses AI to streamline the form creation process. With its user-friendly interface and AI-driven features, Jotform empowers businesses and individuals to create custom forms effortlessly, without the need for coding expertise. By leveraging AI technology, Jotform simplifies data collection, enhances form performance, and delivers a seamless user experience.

Jotform offers its AI Form Generator, which allows users to create forms simply by describing their requirements in natural language. The AI chatbot understands the user’s needs and generates a tailored form with basic fields and customizations, saving time and effort. Jotform’s AI capabilities extend beyond form creation, as it also offers an AI Quiz Generator and an AI Signature Generator, demonstrating its commitment to innovation.

Key Features:

Create custom forms effortlessly by describing your requirements to the AI chatbot.

Jotform’s AI features, such as conditional logic and prefill options, improve form completion rates and user experience.

Collaborates with OpenAI’s ChatGPT for its AI Quiz Generator, ensuring data privacy and security.

Dedicated to expanding its AI capabilities to meet evolving user needs and maintain its competitive edge.

Enables businesses to automate repetitive tasks, streamline workflows, and focus on high-value activities

Visit Jotform →

With the introduction of AI-driven features and the launch of its innovative product, Formless, Typeform is redefining how businesses engage with and gather information from their customers. This AI-powered approach simplifies form creation, enhances user engagement, and delivers a personalized, conversational experience for respondents.

At the forefront of Typeform’s AI innovation is Formless, a product that transcends traditional form structures. Formless creates a dynamic, two-way conversation between businesses and respondents, mimicking human-like interactions. By allowing businesses to train the AI on specific topics, Formless can answer respondents’ questions and provide a tailored experience, adapting to responses and asking relevant follow-up questions.

Typeform’s AI capabilities extend beyond Formless, offering features like question recommendation and optimization to craft well-written, concise questions that boost completion rates. The platform’s Smart Insights tool employs AI to analyze form results, providing user-friendly dashboards with high-level data overviews. Additionally, Typeform’s AI streamlines lead qualification by automatically categorizing respondents based on their answers, ensuring efficient prioritization of high-value leads.

Key Features:

AI-powered product creating dynamic, two-way conversations for personalized experiences.

AI-assisted question optimization for enhanced form completion rates.

AI-driven analysis tool providing user-friendly data dashboards.

Efficient lead qualification through AI-powered response analysis.

Continuous AI development, including workflow automation and natural language data querying.

Visit Typeform →

Formstack is pushing the boundaries of form building by integrating artificial intelligence to create a comprehensive workflow automation solution. Unlike traditional form builders, Formstack’s AI doesn’t just assist in form creation—it transforms the entire data collection and processing lifecycle.

At the core of Formstack’s innovation is its AI-powered workflow designer. This feature analyzes your business processes and automatically suggests optimal form structures and data flows, creating end-to-end solutions rather than isolated forms. For example, it might design a customer onboarding process that seamlessly moves from initial contact form to follow-up surveys and integration with your CRM.

Formstack’s AI also shines in its predictive analytics capabilities. By analyzing historical form data, it can forecast submission patterns, helping businesses prepare for peak times or identify potential drop-offs in engagement. This proactive approach allows companies to optimize their forms and processes continuously, staying ahead of user needs and market trends.

Key Features:

Generates tailored forms based on user prompts.

Allows teams to go from idea to solution quickly, regardless of their technical aptitude.

Suggests well-written and concise questions to enhance form completion rates.

Analyzes form data, identifying patterns and anomalies that provide valuable insights for businesses.

Easily integrate with other business systems, such as CRMs and Formstack Documents, for automatic data population and streamlined workflows.

Visit Formstack →

With its user-friendly interface and AI-driven features, Paperform enables businesses and individuals to create engaging, personalized forms effortlessly, without the need for coding expertise. By leveraging AI technology, Paperform enhances the form-building experience, making it more efficient, intuitive, and tailored to users’ specific needs.

One of Paperform’s standout AI features is its ability to generate forms based on user prompts. Users can simply describe the type of form they need, and Paperform’s AI-powered Form Builder will create a customized form with relevant fields and customizations. This feature takes the heavy lifting out of form creation, allowing users to focus on more strategic tasks while ensuring that the generated forms are optimized for engagement and data collection from the start.

Paperform’s AI capabilities extend beyond form creation, with features like question optimization and data analysis. The platform’s AI can suggest well-written and concise questions that encourage higher form completion rates.

Key Features:

Generates tailored forms based on user prompts.

Create personalized forms with no coding.

Question optimization and data analysis.

Suggests well-written and concise questions to achieve higher completion rates.

Visit Paperform →

Tally is reimagining the form-building landscape with its AI-powered platform, designed to eliminate complexity and streamline the creation process. This innovative tool stands out by focusing on simplicity and user experience, making professional form design accessible to everyone, regardless of technical background.

At the heart of Tally’s approach is its conversational AI interface. Rather than navigating complex menus, users can simply describe their form needs in natural language. The AI interprets these requests, instantly generating tailored forms complete with relevant fields and logic. This collaborative process feels more like working with a skilled assistant than operating software.

Tally’s commitment to privacy sets it apart in the AI form-building space. With European hosting, GDPR compliance, and end-to-end encryption, it offers a secure solution for handling sensitive data. This makes Tally particularly attractive for industries with strict data protection requirements, such as healthcare and finance.

Key Features:

Generates tailored forms based on user prompts, simplifying the form creation process.

Enables the creation of dynamic forms that adapt based on user inputs or external data.

Prioritizes data privacy and security, ensuring GDPR compliance, hosting in Europe, and encrypting form data both in transit and at rest.

Caters to a wide range of industries and use cases.

Easily integrate with popular tools like Notion, Slack, and Airtable, streamlining workflows and automating processes.

Visit Tally →

Wufoo has established itself as a trusted cloud-based form builder, serving over 3 million users including major brands like Amazon and Microsoft. Its interface simplifies the creation of various online forms, from registrations to payment forms, without requiring technical expertise. Wufoo’s strength lies in its user-friendly design, extensive template library, and robust reporting capabilities.

While not heavily AI-focused, Wufoo has recently integrated with include.ai, expanding its automation capabilities. This integration, combined with Wufoo’s existing features like automated database building and script generation, positions it as a powerful solution for efficient data collection and management. Wufoo’s ability to integrate with various third-party apps further enhances its appeal for businesses seeking to streamline their workflows.

Key Features:

Intuitive design for easy form creation and customization.

Visually appealing forms matching brand styles.

Automatic database, backend, and script building.

Connects with various third-party apps for streamlined workflows.

Over 3 million users and a decade of experience.

Visit Wufoo →

Forms.app distinguishes itself with its AI Form Generator, which allows users to create forms simply by describing their requirements in natural language. This innovative approach simplifies the form creation process, making it accessible to users of all technical levels. The platform’s AI capabilities extend to survey and quiz creation, offering specialized tools that quickly generate these types of forms with minimal user input.

The AI technology powering Forms.app continuously learns and improves, providing users with intelligent suggestions and optimizations to enhance form performance and user engagement. With integration capabilities spanning over 500 apps, Forms.app offers a flexible and efficient solution for businesses looking to streamline their data collection processes and form-based workflows.

Key Features:

Create custom forms by describing requirements to AI assistant.

Generate online surveys quickly with AI-powered survey maker.

Create engaging quizzes easily with AI assistance.

Expanding AI capabilities to meet evolving user needs.

Connects with over 500 apps for smooth workflow and data management.

Visit Forms.app →

Landingi combines landing page building with AI-powered form creation, offering a comprehensive solution for businesses aiming to generate leads and drive conversions. Its standout AI features include a text generator that creates compelling form content based on user prompts, and an SEO generator that optimizes forms for search engines. These tools significantly reduce the time and effort required for copywriting and SEO optimization.

Beyond content creation, Landingi’s AI capabilities extend to image processing and language support. An AI-powered background removal tool enhances the visual appeal of forms, while machine learning-powered translations enable the creation of multilingual forms. This combination of features makes Landingi a versatile platform for businesses looking to create high-converting forms and landing pages with a global reach.

Key Features:

Creates compelling form content based on user prompts.

AI-powered generator optimizes content for search engines.

AI tool for enhancing visual appeal of forms.

ML-powered tool for creating multilingual forms.

Combines AI-powered form creation with landing page building.

Visit Landingi →

MakeForms leverages AI to offer a secure and highly customizable form-building experience. Its AI-powered form builder automates the creation process by suggesting relevant questions and providing tailored templates based on user requirements. MakeForms sets itself apart with advanced AI capabilities like facial recognition for Know Your Customer (KYC) processes, ensuring enhanced security and identity verification.

The platform’s AI extends to form logic and data analysis. Conditional logic enables the creation of personalized forms that adapt based on respondents’ answers, while advanced data organization features like table, summary, and BI views allow for effective analysis and visualization of form data. This combination of security, customization, and analytics makes MakeForms a comprehensive solution for businesses requiring sophisticated form-building capabilities.

Key Features:

Suggests relevant questions and provides tailored templates.

Facial recognition for KYC enhances security and identity verification.

Conditional logic creates personalized forms adapting to respondents’ answers.

Data organization and analysis offers table, summary, and BI views for insights.

Includes secure payment collection, team collaboration, and integrations.

Visit MakeForms →

Why You Should Use an AI Form Generator

AI form generators are improving the way we create and manage online forms. These powerful tools leverage artificial intelligence to streamline form creation, making it easier than ever to design beautiful, interactive forms without extensive technical knowledge. By using an AI form builder, you can save time and resources while still creating user-friendly forms that effectively collect data.

One of the key advantages of AI-generated forms is their ability to adapt and improve based on user interactions. These intelligent systems can analyze form completion rates, identify potential roadblocks, and suggest optimizations to enhance the user experience. This means your forms can continuously evolve to become more effective at gathering the information you need, while also providing a smoother experience for your respondents.

Moreover, AI form generators often come with advanced features such as conditional logic, data analysis, and seamless integrations with other business tools. This allows you to create powerful forms that not only collect data but also help you derive meaningful insights from it. Whether you’re building a simple contact form or a complex survey, an AI form generator can help you create unique, engaging forms that stand out and deliver results. By embracing this technology, you’re not just keeping up with the latest trends – you’re positioning your organization at the forefront of efficient, intelligent data collection.

[bfpnola]

I am not Palestinian nor am I Jewish. Be that as it may, I hate settler colonialism, even more so as a brown, bi, genderqueer ‘Afab’ person. I just wanted to say. 1) your post on the topic is more empathetic and insightful than I’ve seen a lot of people be about this over my entire life and I’ve asked questions of both sides, I tend to stay out of the fray cause I don’t feel it my place to speak over Palestinians and Jews (who are critical of Israel). But, do you have any advice for being a better ally to Palestinians and combating anti-semitism and anti Jewish racism in the everyday?

hey sweetheart! thank you for your commitment to the movement and your earnestness. i am not Palestinian or Jewish either, so i did what is always considered best: i asked those who are! that's exactly why our Advocacy Committee within BFP exists :)

from one of our Palestinian youth volunteers:

if you have the money to do so, donate to the cause! the unfortunate truth is that to gain access to various resources, things cost money. more specifically, donate to humanitarian aid funds you've done the research for and are sure are doing work on the ground. even better if you can donate directly to those being affected! this includes Palestinians on the ground but also within the diaspora who need self care items, especially for all the work they've been doing educating others. for example, this is an organization this member volunteers with and trusts:

and these are two amazon lists of Palestinian youth within the diaspora:

share posts by Palestinians! the big thing is really just getting the word out, sharing their perspective. Zionist propaganda is hard to penetrate so the least we can do is uplift their voices by sharing!

from one of our Jewish youth volunteers:

understand that not all Jewish people are Zionists and not all Zionists are Jewish. saying the two are equivalent is not only antisemitic but ignores the blatant statistics, like the growing number of anti-Zionist Jewish young adults in the united states for example, or the fact that the biggest supporters of israel are actually evangelicals.

to that same point, know that israel has been purposefully trying to conflate the two in order to then label anyone who does critique the state as automatically antisemitic. it is a tool.

additionally, be careful with the rhetoric you choose to spread & subscribe to (i.e., watch how they describe israel. do they refer to the people as Jews or Zionists? it can tell you a lot about how educated they are and their vague stance on the matter)

my own additions as a longstanding ally and friend of those involved:

learn your history! there is a clear attempt to distort the history of Palestine. learn what Palestine was like before israel's occupation. learn about the way pioneering Zionists openly called Zionism "colonialism" and didn't even try to hide it. learn about how discussions of the Zionist project were discussed roughly 80 years before the Holocaust ever happened. this does not mean that some Jews did not, in fact, move to Palestine in response to such a horrific event, but in the words of a Jewish mutual of mine, israel's rhetoric literally weaponizes Jewish trauma by conflating these two dates in history.

BDS movement! stands for boycott, divestment, and sanctions!

when possible, actually speak to people of Palestinian descent. like seriously. posts are great, but actually speaking to people who are knowledgeable in real time can be so helpful for getting your questions addressed, so long as you are respectful, of course. a great place to do this, not even to advertise, is actually our Discord server linked in our bio @bfpnola

know that language matters, as inconsequential as it may seem. in the words of my Palestinian, Kashmiri, and Artsakhi friends and/or mutuals, when speaking of occupations, we capitalize the occupied people's country (ex. Palestine) while not doing so for the occupier's (ex. israel) to delegitimize them.

learn about Hamas and its history/purpose. here are my notes on two podcast episodes let by Palestinians:

thank you for your ask! im sure i may think of other things later but these are my answers for now.

-- reaux (she/they)

[womaneng]

Data Science

📌Data scientists use a variety of tools and technologies to help them collect, process, analyze, and visualize data. Here are some of the most common tools that data scientists use:

👩🏻‍💻Programming languages: Data scientists typically use programming languages such as Python, R, and SQL for data analysis and machine learning.

📊Data visualization tools: Tools such as Tableau, Power BI, and matplotlib allow data scientists to create visualizations that help them better understand and communicate their findings.

🛢Big data technologies: Data scientists often work with large datasets, so they use technologies like Hadoop, Spark, and Apache Cassandra to manage and process big data.

🧮Machine learning frameworks: Machine learning frameworks like TensorFlow, PyTorch, and scikit-learn provide data scientists with tools to build and train machine learning models.

☁️Cloud platforms: Cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide data scientists with access to powerful computing resources and tools for data processing and analysis.

📌Data management tools: Tools like Apache Kafka and Apache NiFi allow data scientists to manage data pipelines and automate data ingestion and processing.

🧹Data cleaning tools: Data scientists use tools like OpenRefine and Trifacta to clean and preprocess data before analysis.

☎️Collaboration tools: Data scientists often work in teams, so they use tools like GitHub and Jupyter Notebook to collaborate and share code and analysis.

For more follow @woman.engineer

[techniktagebuch]

2024

Mein Medien-Menü: Zwölf Jahre später

Im Februar 2012 habe ich für Christoph Kochs Reihe "Mein Medien-Menü" beschrieben, wie meine Mediennutzung damals aussah. Diese Serie ist einer der Gründe, warum es das Techniktagebuch gibt. Bis November 2014 sind insgesamt 89 Folgen im Blog von Christoph Koch erschienen. Danach zog das Medienmenü um zu Krautreporter, wo es so aussieht, als seien bis ungefähr 2017 noch mal ziemlich viele Folgen veröffentlicht worden. Ob man die gesammelt irgendwo lesen kann und ob es nach 2017 noch weiterging, weiß ich nicht, weil ein Krautreporter-Abo nicht zu meiner Mediennutzung gehört. (Ohne besondere Gründe, im ersten Krautreporterjahr war ich Unterstützerin. Ich erinnere mich vage an Unzufriedenheit, weshalb ich es danach nicht mehr war. Aber die Details sind leider undokumentiert geblieben.)

Ich habe lange nicht mehr an diesen Bericht gedacht und sehe heute noch mal nach, wie das eigentlich 2012 war und was sich geändert hat.

"Goodreads ist nicht besonders überzeugend, ich kenne nur wenige Menschen, die es nutzen, und die Buchempfehlungen dort sind nur unwesentlich besser als bei Amazon. Aber ich finde es sehr hilfreich, um eine realistische Vorstellung von meinem Leseverhalten zu bekommen. Bis ich damit anfing, hielt ich mich immer noch für denselben Leser wie 1995."

Ich war damals noch ein Leser und keine Leserin. Mit dem generischen Maskulinum habe ich erst viel später aufgehört. Im Techniktagebuch ist zu sehen, wann das passiert ist, meiner Erinnerung nach vielleicht 2018? Irgendwann sehe ich nach und dann steht es hier genauer. Goodreads fand ich zwischen damals und jetzt sehr überzeugend. Ich kenne zwar immer noch nur wenige Menschen, die es nutzen, und in die automatischen Buchempfehlungen habe ich schon lange nicht mehr reingesehen. Aber ich habe dort in den letzten Jahren sehr viele Rezensionen gelesen und das war der Hauptweg, auf dem ich zu neuen Büchern gefunden habe. Allerdings versuche ich gerade, mich (wegen der Amazon-Zugehörigkeit) von Goodreads zu lösen zugunsten von StoryGraph. Da läuft aber gerade erst der Umzug meiner Daten und ich kann noch nichts dazu sagen.

"Meine Papierbücher habe ich in den letzten paar Jahren mit Hilfe des Berliner Büchertischs stark reduziert, von ungefähr zwölf mehrreihig gefüllten Billyregalen bin ich jetzt runter auf sieben halbvolle."

Im Moment sind es vier ganz volle, davon zwei mehrreihig gefüllt. 2019 waren es auch schon nur vier. Was mit den drei anderen passiert ist, weiß ich nicht mehr. Falls es Zuwachs gegeben hat, ist das unfreiwillig passiert, durch eigene Belegexemplare, ungefragt zugeschickte Bücher und Bücher, die ich auf Papier kaufen musste, weil ich sie für die Arbeit brauchte und nicht auf einem digitalen Weg beschaffen konnte. Ich lese jetzt aber viel mehr Bücher als 2012.

Dann geht es im Text von 2012 einen Absatz lang um RSS-Feedreader. Ich habe damals noch den Google Reader genutzt, den Google anderthalb Jahre später eingestellt hat. Mit Feedly, dem Tool, mit dem ich ihn ab Mitte 2013 zu ersetzen versuchte, bin ich nie so richtig warm geworden, er ist 2016 aus meinem Leben verschwunden. Ich habe ihn nicht ersetzt und lebe seitdem feedreaderlos.

"... das, was ich im Netz lese, speist sich jetzt ungefähr (geraten und nicht gemessen, kann also auch ganz anders sein) zur Hälfte aus dem Feedreader und zur Hälfte aus dem Bekanntenkreis via Google+, Twitter und Facebook. "

"Netz" sage ich nicht mehr, seit ich 2021 erfahren habe, dass es ein altmodisches Wort für Internet ist. Ich dachte bis dahin, es sei umgekehrt.

"Ein oder zwei Jahre lang hatte ich mir für die wichtigsten Feeds eine Weiterleitung nach Twitter gebastelt (via Yahoo Pipes und Twitterfeed), aber seit es Google+ gibt, nutze ich Twitter viel weniger und sehe deshalb auch diese Weiterleitung kaum mehr."

Yahoo Pipes! Das war wirklich schön und ich vermisse es heute noch manchmal. Es wurde 2015 eingestellt. Man konnte damit, so ähnlich wie jetzt mit Zapier, andere Internetdinge zusammenstecken, aber mit einer schönen grafischen Oberfläche. Bei Google+ war ich 2011 und offenbar auch noch Anfang 2012 sehr aktiv, aber irgendwann bald danach war es wieder vorbei. Warum, weiß ich nicht mehr, es ist im Techniktagebuch nicht dokumentiert. In meiner Erinnerung wurde Google+ kurz nach dem Start wieder stillgelegt, aber das scheint nicht zu stimmen, in der Wikipedia steht: Schließung 2019. Ich bin danach zu Twitter zurückgekehrt.

Von den Blogs, die mir damals wichtig waren, gibt es ein paar noch, sie sind mir aber unsympathisch geworden (Marginal Revolution, Less Wrong, Overcoming Bias). Andere gibt es nicht mehr (Stefan Niggemeiers Blog, Penelope Trunk). Ich glaube, dass das nicht weiter besorgniserregend ist, die meisten Blogs haben eine begrenzte Lebenszeit aus inhaltlichen wie aus Verfügbare-Lebenszeit-Gründen und es wachsen ja auch wieder neue nach. Im Überschneidungsbereich von "existiert noch" und "wir haben uns nicht weltanschaulich entfremdet, glaube ich", liegt nur ein einziger der erwähnten Blogs: O'Reilly Radar. Ich lese es trotzdem nie. Das hat auch wieder mit dem Verschwinden des Google Readers zu tun. Ich lese wahrscheinlich immer noch so viel in Blogs wie früher, aber nicht mehr regelmäßig in denselben, sondern eben die Beiträge, die mir bis 2022 Twitter heranspülte und seit meinem Umzug Mastodon. Ich merke mir dann nicht, in welchem Blog die standen, und könnte keine Blognamen nennen. Facebook erwähne ich 2012 noch, 2015 habe ich das Facebook-Browsertab geschlossen und 2017 die App vom Handy gelöscht.

Zeitschriften mit der Post bekam ich 2012 noch mehrere, zum Teil wegen Vereinsmitgliedschaften und zum Teil, weil ich sie abonniert hatte. Eins der Abos habe ich gleich nach der Dokumentation im Medien-Menü-Beitrag gekündigt, ein anderes endete etwas später von allein, und die Mitgliedszeitschriften haben sich in den letzten Jahren entweder selbst auf nur-noch-digital umgestellt oder ich habe darum gebeten, nichts mehr auf Papier zu bekommen. Außerdem wird meine Post seit mehreren Jahren direkt an Nathalie weitergeleitet, die sich um meine Papierverwaltung kümmert.

2024 gehört zur finanziellen Seite meines Medien-Menüs, dass ich einige Leute regelmäßig unterstütze bei Patreon, Steady und ähnlichen Plattformen. Ich müsste das mal in einem gesonderten Beitrag genauer aufschreiben, jedenfalls ist es im Moment der Hauptkanal, auf dem Geld von mir zu Kulturschaffenden fließt. Die Newsletter oder Videos, die zu manchen dieser Abos gehören, schaue ich mir aber eigentlich nie an. Es geht mehr ums Prinzip, ich möchte, dass diese Leute weiter Videos machen, Bücher schreiben oder was sie halt so tun.

"Radio habe ich seit den 80er Jahren nicht mehr gehört (traumatische Schulbus-Erlebnisse mit Bayern 3). Eine Tageszeitung hatte ich zuletzt um 1990 im Abonnement. Ich habe aufgehört, fernzusehen, als im deutschen Kabel das britische MTV Europe durch den deutschen Ableger ersetzt wurde, das muss so um 1995 herum gewesen sein. Über Hörbücher und Podcasts weiß ich nichts, ich schlafe aus technischen Gründen beim Zuhören immer sofort ein."

Daran hat sich seit 2012 wenig geändert. Ich war viel im Haushalt meiner Mutter, und dort wird jeden Tag wenigstens eine Stunde Radio gehört (BR Heimat zwischen 22:00 und 23:00). Außerdem ist es mir gelungen, mittelgroße Teile des "Drinnies"-Podcasts zu hören. Eine Änderung meines Mediennutzungsverhaltens sehe ich darin aber nicht, das eine ist Zufall, das andere eine Ausnahme.

Video kommt im Text von 2012 gar nicht vor. Hier hat sich mehr geändert, 2016 habe ich eingesehen, wozu YouTube gut ist, und inzwischen nutze ich es oft, allerdings vor allem in der kleinen Vorschau-Ansicht auf dem Handy, die ungefähr 6x4 cm groß ist, und ohne Ton. Theoretisch folge ich dort zwar ein paar Leuten aus den Bereichen Handwerk (Schreinerei, Metallbearbeitung, Rohrreinigung) und Schlittenhundehaltung, praktisch mache ich davon aber so gut wie nie Gebrauch, es sind Höflichkeits-Abos zur Erfreuung der Youtuber*innen. Ich bin nur da, wenn ich was Bestimmtes suche und gucke dann vielleicht noch ein paar von den Dingen, die YouTube mir vorschlägt. Dabei bin ich inzwischen besser darin geworden, den Vorschlägen zu widerstehen, weil mir YouTube immer gern Katastrophen und Unglücke zeigen möchte und ich aber wirklich nicht noch mehr über scheußliche Tode beim Höhlentauchen wissen will. Lieber würde ich das vorhandene Wissen darüber wieder aus meinem Kopf löschen lassen. Was mir in meinem Medienmenü 2024 fehlt, ist ein Lösch-YouTube zur Entfernung von Informationen.

(Kathrin Passig)

[raziakhatoon]

 Data Engineering Concepts, Tools, and Projects

All the associations in the world have large amounts of data. If not worked upon and anatomized, this data does not amount to anything. Data masterminds are the ones. who make this data pure for consideration. Data Engineering can nominate the process of developing, operating, and maintaining software systems that collect, dissect, and store the association’s data. In modern data analytics, data masterminds produce data channels, which are the structure armature.

How to become a data engineer:

 While there is no specific degree requirement for data engineering, a bachelor's or master's degree in computer science, software engineering, information systems, or a related field can provide a solid foundation. Courses in databases, programming, data structures, algorithms, and statistics are particularly beneficial. Data engineers should have strong programming skills. Focus on languages commonly used in data engineering, such as Python, SQL, and Scala. Learn the basics of data manipulation, scripting, and querying databases.

 Familiarize yourself with various database systems like MySQL, PostgreSQL, and NoSQL databases such as MongoDB or Apache Cassandra.Knowledge of data warehousing concepts, including schema design, indexing, and optimization techniques.

Data engineering tools recommendations:

    Data Engineering makes sure to use a variety of languages and tools to negotiate its objects. These tools allow data masterminds to apply tasks like creating channels and algorithms in a much easier as well as effective manner.

1. Amazon Redshift: A widely used cloud data warehouse built by Amazon, Redshift is the go-to choice for many teams and businesses. It is a comprehensive tool that enables the setup and scaling of data warehouses, making it incredibly easy to use.

One of the most popular tools used for businesses purpose is Amazon Redshift, which provides a powerful platform for managing large amounts of data. It allows users to quickly analyze complex datasets, build models that can be used for predictive analytics, and create visualizations that make it easier to interpret results. With its scalability and flexibility, Amazon Redshift has become one of the go-to solutions when it comes to data engineering tasks.

2. Big Query: Just like Redshift, Big Query is a cloud data warehouse fully managed by Google. It's especially favored by companies that have experience with the Google Cloud Platform. BigQuery not only can scale but also has robust machine learning features that make data analysis much easier. 3. Tableau: A powerful BI tool, Tableau is the second most popular one from our survey. It helps extract and gather data stored in multiple locations and comes with an intuitive drag-and-drop interface. Tableau makes data across departments readily available for data engineers and managers to create useful dashboards. 4. Looker:  An essential BI software, Looker helps visualize data more effectively. Unlike traditional BI tools, Looker has developed a LookML layer, which is a language for explaining data, aggregates, calculations, and relationships in a SQL database. A spectacle is a newly-released tool that assists in deploying the LookML layer, ensuring non-technical personnel have a much simpler time when utilizing company data.

5. Apache Spark: An open-source unified analytics engine, Apache Spark is excellent for processing large data sets. It also offers great distribution and runs easily alongside other distributed computing programs, making it essential for data mining and machine learning. 6. Airflow: With Airflow, programming, and scheduling can be done quickly and accurately, and users can keep an eye on it through the built-in UI. It is the most used workflow solution, as 25% of data teams reported using it. 7. Apache Hive: Another data warehouse project on Apache Hadoop, Hive simplifies data queries and analysis with its SQL-like interface. This language enables MapReduce tasks to be executed on Hadoop and is mainly used for data summarization, analysis, and query. 8. Segment: An efficient and comprehensive tool, Segment assists in collecting and using data from digital properties. It transforms, sends, and archives customer data, and also makes the entire process much more manageable. 9. Snowflake: This cloud data warehouse has become very popular lately due to its capabilities in storing and computing data. Snowflake’s unique shared data architecture allows for a wide range of applications, making it an ideal choice for large-scale data storage, data engineering, and data science. 10. DBT: A command-line tool that uses SQL to transform data, DBT is the perfect choice for data engineers and analysts. DBT streamlines the entire transformation process and is highly praised by many data engineers.

Data Engineering  Projects:

Data engineering is an important process for businesses to understand and utilize to gain insights from their data. It involves designing, constructing, maintaining, and troubleshooting databases to ensure they are running optimally. There are many tools available for data engineers to use in their work such as My SQL, SQL server, oracle RDBMS, Open Refine, TRIFACTA, Data Ladder, Keras, Watson, TensorFlow, etc. Each tool has its strengths and weaknesses so it’s important to research each one thoroughly before making recommendations about which ones should be used for specific tasks or projects.

  Smart IoT Infrastructure:

As the IoT continues to develop, the measure of data consumed with high haste is growing at an intimidating rate. It creates challenges for companies regarding storehouses, analysis, and visualization. 

  Data Ingestion:

Data ingestion is moving data from one or further sources to a target point for further preparation and analysis. This target point is generally a data storehouse, a unique database designed for effective reporting.

 Data Quality and Testing: 

Understand the importance of data quality and testing in data engineering projects. Learn about techniques and tools to ensure data accuracy and consistency.

 Streaming Data:

Familiarize yourself with real-time data processing and streaming frameworks like Apache Kafka and Apache Flink. Develop your problem-solving skills through practical exercises and challenges.

Conclusion:

Data engineers are using these tools for building data systems. My SQL, SQL server and Oracle RDBMS involve collecting, storing, managing, transforming, and analyzing large amounts of data to gain insights. Data engineers are responsible for designing efficient solutions that can handle high volumes of data while ensuring accuracy and reliability. They use a variety of technologies including databases, programming languages, machine learning algorithms, and more to create powerful applications that help businesses make better decisions based on their collected data.

[mightyaphrodytee]

There were a few times in my life when music changed for me—what I responded to changed slowly over time, but yeah, there were definite infusions of NEW that veered off on paths maybe not so well-trodden, but that nonetheless stood out as touchstones in my ~~~dramatic half-whisper~~~ journey through 🎶MUSIC 🎼

1977: Heard the best of what’s now considered “classic rock” as it existed at the time, when it was just called “Rock” or “Heavy Metal” or “Prog.” Bands like Rush, Boston, Yes, Queen, Led Zeppelin, Black Sabbath, Pink Floyd, that didn’t get a lot of airplay on the Top 40 stations I’d exclusively listened to. It was thrilling. I caught up on ten years of ignorance in like, 9 months. But I kinda missed out on punk because of that immersion, thanks to my new besties.

1982: Heard my first indie/alternative (“new wave” to some) music and fell hard. The Cure, The English Beat, Joy Division, Kim Wilde, Elvis Costello, U2, Talking Heads, etc. when we moved to Colorado. The availability of some truly esoteric indie music via the Boulder station KBCO was legendary. We had three or four stations in addition to that one! Spoiled! The eighties, man. R.E.M.!!! The music in the clubs was what was on the radio was what was on MTV—you couldn’t escape it, so this huge subset of the rock-listening population were all listening to the big hits at the same time. Madonna, Dire Straits, The Eurythmics, Prince, Duran Duran, Pretenders, Bon Jovi. EVERYBODY knew the hits of the eighties.

1991: Heard “Smells Like Teen Spirit” on the car radio driving through Austin, and both my companion and I were immediately silenced by that intro, and by the end, we were like “What just happened?” just in total delight/light shock…did he really just scream about a mulatto? Who talks like that in 1991, sir? But we just immediately knew this was gonna be huge, and it was, and then came grunge and grunge-lite for the rest of the decade. Soundgarden, STP, Bush, Incubus, Alice In Chains, Pearl Jam, Nirvana (for such a goddamned short time, it’s insane to look back and realize we had so few years with him!)

For some people, life is unbearable without having their consciousness altered in some way. Drugs being one of those ways.

2003: Heard “Caring Is Creepy” by The Shins on a 4-hour “New Alternative” loop XM Radio had handed out as a free trial. Those songs on that loop woke me up to the possibility of new sounds that hit that same place in me as the best of the 80’s and 90’s. I remember Doves “Pounding”, which was used in an episode of The Consultant on Amazon Prime just this week (I shrieked!), “Silver Spoon” by Bis, “Shapes” by The Long Winters, The Postal Service, Death Cab For Cutie…wish I could remember them all. Bruce Springsteen’s Magic album had a song that was my most played for a few years in the aughts—“Radio Nowhere”, which I first heard on that XM trial loop and loved so much I bought the whole album. On iTunes. Still have it. Saw Garden State, heard “Caring Is Creepy” on the soundtrack (again—i shrieked!), and “New Slang,” and fell for them even harder.

Now I listen to what I used to hate (classic rock), but my fairly narrow preference window means I don’t SAY I listen to classic rock, because except for YouTube, I only listen to Radiohead, some Tool, some Metallica most days.

My life is now just mainly Radiohead with a few dollops of all the songs I’ve loved before, from every decade that rock and roll has been rock and roll with ALL its subgenres, heavy on Tool and Metallica as of late.

I can’t even tell what popular music today even is. It all sounds like video game background to me.

Will you still need me

Will you still feed me

When I’m 64?

[nitor-infotech]

How do Big Data and AI work Together?

Data is the new oil, and AI is the engine driving its power. Well, we are not saying this, but a recent study says that - 79% of organizations have gained new insights and achieved significant improvements in their analytical capabilities by leveraging the power of AI. So, in today’s rapidly evolving technological landscape, the fusion of artificial intelligence (AI) and big data is set to expand the boundaries of what’s possible.

In this 3-minute read, you'll explore the relationship between AI and big data, discover their capabilities, and some real-world examples.

So, let’s get started!

Relation between AI and Big Data

In the past decade, businesses amassed vast amounts of data—marking the big data revolution. However, storing this data wasn't enough. To truly harness its value, companies slowly turned towards advanced AI and machine learning techniques.

Machine learning uses algorithms to find patterns and insights, unlike traditional methods that follow fixed rules. On the other hand, big data provides the vast amounts of information needed for these algorithms to work effectively.

This is how they work:

· Data Collection and Storage: Big data systems collect and store vast amounts of information from multiple sources using various frameworks like Hadoop or cloud-based data lakes.

· Data Processing: Tools like Apache Spark preprocess and structure this data for analysis.

· Feature Extraction: Next, various techniques like Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE) identify relevant features from raw data, preparing it for AI models.

· Machine Learning: Algorithms, such as neural networks or decision trees, analyze data to uncover patterns and build predictive models.

· Model Training and Validation: AI models are trained and validated on subsets of data to ensure accuracy.

· Deployment: Trained models are deployed for real-time predictions using platforms like TensorFlow Serving.

· Feedback Loop: Performance is monitored, and the models are then updated based on new data.

Hope you’re clear about their relationship and working. If yes, next, let’s talk about the exceptional capabilities it can bring to your table.

Leveraging Big Data with AI’s Power

Here are the top 5 benefits of combining AI with Big Data’s capabilities:

1. Comprehensive Customer Insights: Digital footprints are expanding, allowing companies to gain deeper customer insights using automated analytics over data lakes, revolutionizing customer understanding.

2. Interactive Data Engagement: Generative AI enables easy data interactions through conversational prompts, making data extraction accessible to all users.

3. Tailored Recommendations: AI helps create detailed user profiles for personalized content and product suggestions, improving relevance across various industries.

4. Boosted Customer Acquisition and Retention: AI and big data refine customer insights to enhance products and strategies, boosting satisfaction, conversions, and loyalty.

5. Advanced Fraud Detection: Big data analytics detect and prevent fraud by analyzing large data volumes for anomalies. Thus, enhancing cybersecurity.

Onwards to some real-life examples!

AI and Big Data in Action: Inspiring Examples

Get inspired with these real-world examples of AI-powered big data integration:

· Netflix uses machine learning to tailor recommendations for each user, enhancing their experience and increasing engagement on the platform.

· Google leverages machine learning to deliver personalized features such as predictive text and optimized directions. Additionally, it uses big data to develop generative AI LLMs, improving search functionality.

· Amazon utilizes AI and big data to offer hyper-personalized shopping experiences. By analyzing user behavior and purchase history, its recommendation engine suggests products, boosting both user satisfaction and sales.

So, as we move forward, the fusion of machine learning, big data, and advanced analytics will be crucial for informed decision-making. Organizations that overlook this integration risk falling behind in the digital transformation race.

Secure your business today and enjoy growth - reach us at Nitor Infotech.

[ebitans-2]

Best Affiliate Programs for Beginners: A 2024 Guide

Affiliate marketing is among the most popular ways to generate passive income online. It allows you to earn a commission by promoting products or services from other companies. Choosing the right affiliate program can be overwhelming for a beginner due to the abundance of options. This guide will help you understand how to start with affiliate marketing and recommend some of the best affiliate programs for beginners.

What is Affiliate Marketing?

Affiliate marketing is a performance-based marketing strategy where you, as an affiliate, promote a company’s product or service. When a customer makes a purchase through your unique affiliate link, you earn a commission. It’s a win-win scenario — the company gets more sales, and you earn money without having to create your own product or service.

Here’s how affiliate marketing works:

Sign up for an affiliate program: The company provides a unique tracking link.

Promote products/services: You share your link through your website, blog, social media, email, etc.

Earn commissions: When someone clicks on your link and completes a purchase, you earn a percentage of the sale.

Why is Affiliate Marketing Great for Beginners?

Low startup cost: You don’t need to invest in inventory, manufacturing, or shipping. You can start affiliate marketing with little to no upfront cost.

Flexible work schedule: You can work from anywhere at any time, making it ideal for those looking for a side hustle or full-time income.

No customer support: Unlike running a business, you don’t have to deal with customer service issues like complaints, returns, or refunds.

High earning potential: Some affiliates make hundreds or thousands of dollars monthly. Your earning potential grows as you build trust and an audience.

What to Look for in an Affiliate Program?

When selecting an affiliate program, it’s crucial to consider several factors:

Commission Rates: What percentage of the sales do you earn? Commission rates typically range from 5% to 50%, depending on the product and niche.

Cookie Duration: When a visitor clicks on your affiliate link, a cookie is stored on their device. The cookie duration refers to how long you can earn a commission from that click. Longer cookie durations (30–90 days) are better.

Reputation of the Brand: Promote reputable brands offering high-quality products or services. It’s easier to sell something you believe in.

Payment Terms: Check the payment threshold and how often you’ll be paid (monthly, bi-weekly, etc.). Make sure your payment methods (PayPal, bank transfer, etc.) suit you.

Affiliate Support and Resources: Look for programs with tools like banners, product data, or educational materials to help you succeed.

Best Affiliate Programs for Beginners in 2024

1. Amazon Associates

Commission Rate: 1% – 10% Cookie Duration: 24 hours Payment Method: Direct deposit, check, or gift card

Amazon Associates is one of the most popular affiliate programs for beginners due to its vast product selection. You can promote virtually any product sold on Amazon, from electronics to books to kitchen appliances. The commission rates vary by category, and while they are generally lower than other programs, the high conversion rate on Amazon makes it a solid option.

Why It’s Great for Beginners:

Trusted brand with millions of products

Simple sign-up process

Easy to integrate with your blog or website

2. ShareASale

Commission Rate: Varies (typically 5% – 30%) Cookie Duration: 30–90 days Payment Method: PayPal, bank transfer

ShareASale is a large affiliate network that connects you with thousands of merchants across different niches. Whether you’re into fashion, home decor, or digital services, you can find a suitable affiliate program on ShareASale. The platform is easy to use and provides detailed performance analytics.

Why It’s Great for Beginners:

Access to a wide variety of merchants

Great for niche websites

Easy-to-use dashboard for tracking

3. CJ Affiliate (formerly Commission Junction)

Commission Rate: Varies by merchant Cookie Duration: Typically 30 days Payment Method: Direct deposit, check

CJ Affiliate is another major affiliate network, similar to ShareASale. It partners with well-known brands like GoPro, Overstock, and Lowes. CJ Affiliate is a good choice if you’re looking to promote both physical and digital products. It offers powerful tracking tools to monitor your performance and optimize your campaigns.

Why It’s Great for Beginners:

Well-known brands and products

Advanced tracking and reporting

Reliable payment system

4. Rakuten Advertising

Commission Rate: Varies by merchant Cookie Duration: Typically 30 days Payment Method: PayPal, direct deposit

Rakuten Advertising is one of the largest affiliate networks in the world and partners with brands like Walmart, Best Buy, and Macy’s. It offers a user-friendly interface and a wealth of marketing tools, including banner ads and product links.

Why It’s Great for Beginners:

Trusted global brands

User-friendly platform

Good customer support

5. ClickBank

Commission Rate: 5% – 75% Cookie Duration: 60 days Payment Method: PayPal, direct deposit

ClickBank specializes in digital products like e-books, software, and online courses, making it a popular choice for content creators, bloggers, and marketers in niches like health, finance, and self-improvement. Some of the products offer very high commissions, which can translate into significant earnings.

Why It’s Great for Beginners:

High commission rates on digital products

Suitable for content creators and bloggers

Easy to find profitable niches

6. Awin

Commission Rate: Varies (typically 5% – 50%) Cookie Duration: 30 days Payment Method: PayPal, bank transfer

Awin is a global affiliate network with over 16,000 advertisers, including Etsy, Under Armour, and HP. It caters to affiliates in various niches, from travel to retail to finance. Awin also has a great reputation for paying on time and offering transparent reports.

Why It’s Great for Beginners:

Large selection of advertisers

Global reach

Great affiliate support

7. Fiverr Affiliates

Commission Rate: $15 – $150 per referral Cookie Duration: 30 days Payment Method: PayPal, direct deposit

Fiverr is a popular marketplace for freelancers offering services like graphic design, writing, and digital marketing. The Fiverr affiliate program allows you to earn a flat rate per sale depending on the service package purchased. It’s ideal if you have a blog or audience focused on freelancing, entrepreneurship, or digital services.

Why It’s Great for Beginners:

Flat-rate commission for easy tracking

Promote a trusted, growing platform

High conversion rates

8. Bluehost Affiliate Program

Commission Rate: $65 per sale Cookie Duration: 90 days Payment Method: PayPal, check

Bluehost is one of the leading web hosting companies, and its affiliate program is especially popular among bloggers and website owners. You earn a flat $65 commission for every new customer who signs up for Bluehost hosting through your affiliate link. The long cookie duration also increases your chances of earning a commission.

Why It’s Great for Beginners:

High one-time commission

Long cookie duration

Ideal for bloggers and website owners

How to Succeed as a Beginner Affiliate Marketer

1. Choose a Niche

Your niche is the specific area or industry you’ll focus on. It’s important to choose a niche that you’re passionate about and knowledgeable in. Popular niches include health, personal finance, technology, and fashion.

2. Build an Audience

Your success as an affiliate marketer depends largely on your audience. Start by creating valuable content that solves problems or provides insights related to your niche. Building trust with your audience is key to convincing them to purchase through your affiliate links.

3. Optimize for SEO

Search engine optimization (SEO) is essential for driving organic traffic to your affiliate content. Learn the basics of SEO, such as keyword research, on-page optimization, and link building to improve your chances of ranking in search engines.

4. Use Multiple Platforms

Don’t rely on just one platform to promote your affiliate links. Use a combination of platforms like blogs, YouTube, social media, and email marketing to reach a wider audience.

5. Test and Optimize

Experiment with different types of content (reviews, tutorials, comparisons) to see what resonates with your audience. You can track your performance using the analytics tools provided by the affiliate networks and make adjustments to optimize your conversions. see more>>

[carrergrowth]

The Best Tech Careers for the Future: What You Need to Know

The technology sector is undergoing rapid evolution, creating a plethora of exciting and lucrative career opportunities. As businesses increasingly rely on technology to drive innovation and efficiency, the demand for skilled tech professionals continues to soar. Here, we explore some of the best tech careers for the future, and why they are poised to be top tech jobs of the future.

Artificial Intelligence (AI) and Machine Learning Engineer 

AI and machine learning are at the forefront of technological advancement. Professionals in this field develop and implement intelligent systems that can learn and adapt. Their work encompasses everything from developing algorithms to training models and deploying AI solutions. 

Skills Required:

Proficiency in programming languages like Python, R, and Java.

Strong understanding of algorithms, data structures, and statistical analysis.

Experience with machine learning frameworks like TensorFlow, Keras, and PyTorch.

Why It’s a Future Tech Career: AI and ML are transforming industries by automating processes, enhancing data analysis, and improving decision-making. As businesses increasingly rely on these technologies, the demand for AI and ML engineers is expected to soar, making it one of the most in-demand tech jobs for the future.

Cybersecurity Specialist 

As cyber threats become increasingly sophisticated, the need for skilled cybersecurity professionals is paramount. Cybersecurity specialists protect digital assets, develop security strategies, and respond to cyberattacks. 

Skills Required:

Knowledge of network security, encryption, and penetration testing.

Familiarity with cybersecurity tools and technologies.

Strong analytical and problem-solving skills.

Why It’s a Future Tech Career: With the rise of cyberattacks and the increasing value of data, cybersecurity is a top priority for organizations worldwide. This role is not only one of the highest-paying tech jobs of the future but also essential in maintaining the security of digital assets, making it a highly sought-after career path.

Data Scientist 

Data scientists are the architects of insights, extracting meaningful information from vast datasets. They employ statistical and machine learning techniques to uncover patterns, trends, and correlations. As data continues to accumulate at an unprecedented rate, the demand for skilled data scientists will only increase.

Skills Required:

Expertise in statistical analysis and data mining.

Proficiency in programming languages like Python and R.

Experience with data visualization tools like Tableau and Power BI.

Why It’s a Future Tech Career: In the era of big data, organizations are increasingly relying on data-driven insights to guide their strategies. Data scientists are essential in extracting valuable information from vast amounts of data, making this one of the best tech jobs for the future.

Cloud Computing Engineer 

These professionals are responsible for designing, implementing, and managing cloud-based solutions. Their expertise in cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform is essential for businesses looking to leverage the benefits of cloud computing.

Skills Required:

Proficiency in cloud platforms like AWS, Azure, and Google Cloud.

Experience with DevOps practices and tools.

Why It’s a Future Tech Career: As more companies migrate to the cloud, the demand for skilled cloud computing engineers continues to grow. This role is pivotal in enabling businesses to scale their operations and improve efficiency, making it one of the top tech jobs of the future.

Robotics Engineer 

As automation and robotics gain traction across industries, the role of robotics engineers becomes increasingly important. They design, develop, and maintain robots for various applications, from manufacturing to healthcare. With advancements in AI and ML, robotics is poised for significant growth, creating exciting career opportunities.

Skills Required:

Strong understanding of mechanical and electrical engineering principles.

Proficiency in programming languages like C++ and Python.

Experience with robotics frameworks and simulation tools.

Why It’s a Future Tech Career: Robotics is revolutionizing industries by automating tasks and improving precision. As technology advances, the role of robotics engineers will become increasingly important, making it one of the future tech jobs in demand.

Blockchain Developer 

Blockchain technology is revolutionizing industries from finance to healthcare. Blockchain developers are responsible for designing and building blockchain applications and platforms. As blockchain adoption continues to expand, the demand for skilled blockchain developers is expected to soar.

Skills Required:

Proficiency in blockchain platforms like Ethereum and Hyperledger.

Knowledge of cryptography and decentralized systems.

Experience with smart contract development.

Why It’s a Future Tech Career: As businesses from finance to supply chain management explore blockchain applications, the demand for skilled blockchain developers is set to rise, making it one of the highest-paid tech jobs in the US.

Network Administrator 

Network administrators play a critical role in ensuring the smooth operation of computer networks. They design, implement, and maintain network infrastructure, troubleshoot issues, and provide technical support.

Skills Required:

Knowledge of network protocols and hardware.

Experience with network security and administration tools.

Why It’s a Future Tech Career: With the increasing reliance on digital infrastructure, the role of network administrators is more critical than ever. This position is essential in maintaining robust and secure networks, making it a key tech job of the future.

Mobile App Developer

The proliferation of smartphones has led to a surge in demand for mobile app developers. These professionals create and develop applications for various platforms, such as iOS and Android. 

Skills Required:

Proficiency in programming languages like Java, Swift, and Kotlin.

Knowledge of mobile app development frameworks.

Experience with UI/UX design principles.

Why It’s a Future Tech Career: With the rise in the use of mobile devices, the demand for innovative mobile apps continues to grow. Mobile app development is one of the top tech jobs of the future, offering numerous opportunities for creativity and impact.

Conclusion

The tech industry is dynamic and ever-evolving, offering a plethora of exciting career opportunities. You can position yourself for success in the future by staying updated on the latest trends and developing in-demand skills. Whether you are passionate about AI, cybersecurity, or cloud computing, the tech industry offers diverse career paths for individuals with the right skills.

In-demand tech jobs in the US and around the world are constantly evolving, but roles like AI engineers, data scientists, and cybersecurity specialists are set to remain at the forefront. By acquiring the necessary skills, you can position yourself for success in these top tech jobs of the future.

[farasexcelr]

Emerging Trends in Data Analytics Course in Kolkata

As data analytics continues to evolve, so do the educational programs designed to equip professionals with the skills they need. The landscape of a Data Analytics Course in Kolkata is undergoing significant changes, reflecting broader industry trends and technological advancements. These emerging trends are shaping how data analytics is taught and practiced, offering new opportunities and challenges for students and professionals alike. Here’s an in-depth look at the emerging trends in Data Analytics Courses in Kolkata and how they are influencing the field.

1. Integration of Advanced Analytics Techniques

Traditional data analytics courses often focused on basic tools and methods. However, the modern Data Analytics Course in Kolkata increasingly incorporates advanced analytics techniques. Students now learn not only about descriptive and diagnostic analytics but also about predictive and prescriptive analytics. Techniques such as machine learning, artificial intelligence, and advanced statistical modeling are becoming integral parts of the curriculum.

Machine learning, for instance, enables analysts to build predictive models that can forecast future trends based on historical data. This is particularly valuable in fields like finance, healthcare, and e-commerce, where accurate predictions can drive strategic decisions. By incorporating these advanced techniques, Data Analytics Courses in Kolkata are preparing students for the complexities of contemporary data challenges.

2. Emphasis on Big Data Technologies

With the explosion of data, the need for handling vast amounts of information has grown significantly. As a result, Data Analytics Courses in Kolkata are increasingly focusing on big data technologies. Courses now include training on tools and frameworks such as Hadoop, Spark, and Kafka, which are essential for processing and analyzing large datasets.

Big data technologies allow analysts to manage and extract insights from data that is too large or complex for traditional data processing tools. Understanding how to leverage these technologies is crucial for working with big data environments, which are becoming more common in industries like retail, telecommunications, and healthcare.

3. Enhanced Focus on Data Visualization

Data visualization remains a critical component of data analytics, but the tools and techniques are evolving. Modern Data Analytics Courses in Kolkata place a strong emphasis on advanced data visualization tools and techniques. Platforms like Tableau, Power BI, and D3.js are increasingly featured in the curriculum, offering students the skills to create interactive and dynamic visualizations.

Effective data visualization helps in communicating complex insights clearly and engagingly. The ability to present data in a visually appealing manner is essential for making data-driven decisions and persuading stakeholders. As a result, courses are now emphasizing not just the technical skills of using these tools but also the principles of effective data storytelling.

4. Integration of Cloud Computing

Cloud computing has revolutionized how data is stored, processed, and analyzed. Data Analytics Courses in Kolkata are incorporating cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud into their programs. These platforms offer scalable resources for data storage and processing, enabling analysts to work with large datasets and perform complex analyses more efficiently.

Cloud computing also facilitates collaboration and data sharing, allowing teams to work together on data projects regardless of their physical location. By integrating cloud computing into the curriculum, Data Analytics Courses in Kolkata are preparing students for the modern, cloud-based data environments that are increasingly common in the industry.

5. Focus on Data Ethics and Privacy

With the increasing focus on data collection and analysis, ethical considerations and privacy concerns are more important than ever. Data Analytics Courses in Kolkata are addressing these issues by incorporating modules on data ethics, governance, and privacy regulations. Understanding the ethical implications of data use and complying with regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) is crucial for ensuring responsible data practices.

Courses are teaching students about the importance of data security, anonymization, and consent. This focus on ethics helps ensure that future data analysts are not only skilled in technical aspects but also aware of the broader implications of their work.

6. Real-World Case Studies and Industry Collaborations

To bridge the gap between theory and practice, Data Analytics Courses in Kolkata are increasingly incorporating real-world case studies and industry collaborations into their programs. By working on projects provided by industry partners or analyzing real datasets, students gain practical experience and insights into how data analytics is applied in different sectors.

Collaborations with local businesses and organizations offer students valuable opportunities to work on actual data problems and contribute to ongoing projects. This hands-on experience is essential for developing problem-solving skills and understanding industry-specific challenges and practices.

7. Incorporation of Interdisciplinary Approaches

Data analytics is increasingly being integrated with other disciplines to provide a more holistic approach to problem-solving. For example, Data Analytics Courses in Kolkata are exploring intersections with fields such as business intelligence, operations research, and behavioral science. This interdisciplinary approach allows students to apply data analytics techniques to a wide range of contexts and problems.

By incorporating insights from related fields, students can develop a more comprehensive understanding of how data analytics can be used to address complex challenges and drive strategic decisions. This approach also enhances their ability to work in diverse and cross-functional teams.

8. Growth of Data Science and Analytics Specializations

As data analytics evolves, so does the range of specializations within the field. Data Analytics Courses in Kolkata are offering specialized tracks or electives that focus on niche areas such as financial analytics, healthcare analytics, marketing analytics, and more. These specializations allow students to tailor their learning experience to specific interests and career goals.

Specializations provide in-depth knowledge and skills related to particular industries or types of data analysis, making graduates more competitive for specialized roles. This focus on areas like financial modeling, patient data analysis, or customer behavior analytics prepares students for targeted career paths within the broader data analytics field.

9. Integration of Artificial Intelligence and Automation

Artificial intelligence (AI) and automation are becoming increasingly important in data analytics. Data Analytics Courses in Kolkata are incorporating AI-driven tools and techniques that automate routine tasks and enhance analytical capabilities. For example, AI can be used to automate data cleaning processes, identify patterns in large datasets, and generate predictive models with minimal manual intervention.

Understanding how to leverage AI and automation technologies allows analysts to work more efficiently and focus on higher-level strategic tasks. Courses are teaching students how to integrate these technologies into their workflows and harness their potential to drive insights and innovation.

10. Emphasis on Soft Skills and Communication

While technical skills are essential, the ability to communicate data insights effectively is equally important. Modern Data Analytics Courses in Kolkata are placing a greater emphasis on developing soft skills such as communication, teamwork, and problem-solving. Courses are including modules on how to present data findings to non-technical stakeholders, craft compelling data stories, and work collaboratively in team settings.

Effective communication is crucial for ensuring that data-driven insights are understood and acted upon by decision-makers. By focusing on these soft skills, Data Analytics Courses in Kolkata are preparing students to excel not just in technical roles but also in positions that require interaction with diverse audiences.

Conclusion

The Data Analytics Course is evolving rapidly to keep pace with emerging trends and technological advancements. From integrating advanced analytics techniques and big data technologies to focusing on data ethics and interdisciplinary approaches, these trends are shaping the future of data analytics education. By understanding and embracing these trends, students can gain a competitive edge and be well-prepared for the dynamic and growing field of data analytics. As the industry continues to evolve, staying updated with these trends will be crucial for leveraging data to drive meaningful insights and impact.

Name: ExcelR- Data Science, Data Analyst, Business Analyst Course Training in Kolkata

Address: B, Ghosh Building, 19/1, Camac St, opposite Fort Knox, 2nd Floor, Elgin, Kolkata, West Bengal 700017

Phone: 08591364838

[pandeypankaj]

What tools do data scientists use

There are a wide variety of tools in use when data scientists analyze and manipulate data. These tools can be placed under several categories as follows: 

Programming languages 

Python: Because of its flexibility and richness of supporting libraries like NumPy, Pandas, Matplotlib, Scikit-learn, it is widely applied in tasks related to Data Analysis, Machine Learning, Data Visualization.

R: It is another language for statistical computing and data analysis. Rich ecosystem of packages for doing a wide variety of tasks.

SQL: Essential when working with relational databases; extracting data for analysis.

Data Analysis and Visualization Tools

Jupyter Notebook: An interactive environment that puts forward code, text, and visualizations all in one. Usually used for data exploration and prototyping.

Tableau: A business intelligence tool, very competent at data visualization. It enables the construction of interactive dashboards and reports.

Power BI: Business intelligence tool targeted at business data visualization and analysis.

Matplotlib, Seaborn: Python libraries to create custom visualizations.

ggplot2: Elegant Graphics for Data Analysis R Package

Machine Learning Libraries

Scikit-learn is a Python library that generalizes the needed algorithms for regression and classification, and other problems in unsupervised machine learning, such as clustering and dimensionality reduction.

TensorFlow: An open-source framework mostly used for building and training deep neural networks for a variety of applications, from research to production.

PyTorch is one of the most popular deep learning frameworks due to flexibility through dynamic computational graphs.

Keras: This is a high-level API run on top of either TensorFlow or Theano, making it much easier to build and train a neural network. 

Cloud Platforms

Amazon Web Services, Google Cloud Platform, Microsoft Azure: All of them offer cloud services that range from data storage and processing to their analysis, thus having data warehouses, machine learning platforms, and big data tools.

Version Control: Git is a well-known VCS used for administering code and data to ensure collaboration with an option for tracking changes. 

Other Tools

Data cleaning and preparation: OpenRefine and Trifacta are tools for preparing and cleaning data so that it could be used.

Database Management: MySQL, PostgreSQL, and MongoDB are some tools that manage and store data.

The choice of the tools most often is determined by the particular needs of the project, team skills, and some preferences within the company. Many data scientists use several tools to reach their target effectively.

[jcmarchi]

John Forstrom, Co-Founder & CEO of Zencore – Interview Series

New Post has been published on https://thedigitalinsider.com/john-forstrom-co-founder-ceo-of-zencore-interview-series/

John Forstrom, Co-Founder & CEO of Zencore – Interview Series

Zencore is a premier Google Cloud consulting and engineering partner, empowering organizations to succeed through expert guidance, comprehensive services, and a relentless focus on risk reduction and client success.

John Forstrom is Zencore’s C-Founder and CEO, he is focused on helping companies make the transformation to cloud based services.

An early believer in Cloud, John joined AWS cloud management software company RightScale in 2009. While many were doubting the use of cloud computing beyond startups, this experience provided him with a front row seat to the shadow adoption of AWS and value of IaaS in large organizations.

In 2013 John joined Google Cloud as part of the initial business team working with product and engineering on the strategy for large enterprises and digital natives.

When John is not making all the connections between Zencore’s customers, partners and Google he can be found on the nearest body of water (surfing, fishing, swimming, paddling).

For over 5 years you worked at Google Cloud, what were some of your responsibilities and what were some of the key highlights from this period?

I joined Google Cloud in September of 2013 when the Cloud division was just a small startup inside of Google. I was one of the first external hires for a business development team that worked with product and engineering to acquire the initial large, strategic customers.

It was a pretty unique time at Google Cloud in which a few hundred of us (now the business is 35k+ employees) were working hand in hand to compete against AWS, which at the time had a much more mature offering. We were 100% focused on the customer and acted as trusted advisors to the early adopters. These companies knew Google Cloud didn’t have feature parity with Amazon, but found value in having a seat at the table as Google built their products and prioritized features.

The highlight for me was in 2015 when I secured a contract for one of the first billion dollar revenue Google Cloud customers.

Can you share more about the genesis of Zencore and what motivated you as a former Google insider to start a company focused exclusively on Google Cloud services?

I think what we have created at Zencore is pretty special, but the concept is rather simple. More than half the company is ex-Google and we have lived and breathed the complexity of clients going from zero to having a significant footprint in Google Cloud.

We took that experience from inside the machine and created a company to solve the major challenges that clients face as they start their journey or ramp on Google Cloud. For me personally and many of us at Zencore it’s refreshing to not have any limitations between us and doing the right thing for customers every time. We make fast decisions and get the right people involved. Zencore is designed to be a throwback to those early days of Google Cloud.

Additionally, our experience with the partner ecosystem during our time at Google consisted mainly of partners who didn’t start with Cloud. So many of Google’s partners started with Workspace, AWS or IT services and extended that to a Google Cloud practice. The ecosystem has definitely matured, but the opportunity for us was to create a business focused only on Google Cloud engineering from the beginning. Our premise was a partner organization that does one thing really really well would make the biggest impact for Google and its customers.

Zencore has chosen to specialize solely in Google Cloud from its inception. What unique opportunities and challenges does this specialization present in the rapidly evolving cloud market?

When you align your company to a single vendor, there is inherent risk in that approach. However, the risk is not significant given Google Cloud’s growth, broad data and infrastructure product portfolio and investment in Gen AI. We are still relatively early in the global adoption of public cloud services and we are very comfortable betting on Google as one of the two long term winners.

The upside to having an entire company focusing on one thing is we are all rowing in the same direction all day, every day. The collaboration between our engineers is such a powerful part of our culture and that only comes from everyone working to solve similar challenges with our clients. When you have delivered hundreds of Google Cloud infrastructure, data and Gen AI projects, there’s not a lot that we haven’t seen which is really powerful when you are working on a complex, high risk engagement.

You are right that the market moves very quickly and we feel like that singular focus on Google allows us to stay current and provide the most value to our clients.

You emphasize a customer-centric and opinionated approach in your services. How does this philosophy translate into tangible benefits for your clients, especially when considering the integration of open-source solutions?

Zencore’s clients are buying experience from a trusted advisor. When they start a project that has significant risk, they want to know that we are 100% aligned with their interests and sometimes that includes sharing some hard truths. Many times the recommendations we make are to not use a Google Cloud native product because an open source option is the best solution. I think that scenario is more rare than you would think. Google has done a really good job of building managed products on top of widely adopted open source solutions that have low operational overhead and are integrated well with the rest of the platform.

But in each one of these conversations we lay out the benefits and challenges of all the options based on real life experience. The client benefits from this approach when speed is critical. There are so many decisions to make and when we recommend a Google Cloud native product for example, the client doesn’t need to spend time second guessing the decision or wasting cycles doing an evaluation. They know we bring an independent, experienced lens to every decision we make.

Your innovative support model that bypasses traditional ticketing systems has been praised by many. Could you elaborate on how this model enhances operational efficiency and client satisfaction?

I like to joke that one of the biggest benefits of working with Zencore is that none of us have a professional services background. The reality is that we don’t do things because that’s the way they have always been done. Our reseller support offering is a great example of one area in which we have taken an innovative approach.

Many of our clients are mid-to-large size software companies. They have experienced engineers, want to move fast, but sometimes they get stuck.

The last thing they want to do when they have a consultative question is to open a ticket, get triaged by an inexperienced support rep, escalate and have that process take a day or two. It’s a total waste of their time and they end up not engaging with a partner’s support offering.

So we created a model to fit into how they work today. Every client get’s a dedicated Slack channel. On the backend of that channel is the entire engineering staff at Zencore.

So when you ask us a deeply technical question, in 15-30 minutes you are Slacking with an experienced cloud engineer or architect directly who will help to unblock your challenge. In addition, many of the questions we receive are less Google Cloud related than they are about the technology that the customer is connecting to Google like Terraform or a particular CI/CD product. It’s that intersection of the customer’s stack and Google Cloud that can be the most complex.

Direct access to our engineers is like gold to our clients. Rather than struggle with an issue, search stack overflow and get frustrated, they ping a channel and immediately get help from an engineer who has worked on dozens of complex projects.

Our clients have described it as “the next best thing to having direct Slack access with Google.”

What are the most common pitfalls companies face when migrating to cloud technologies, and how does Zencore help navigate these challenges?

We have thought a lot about this question and last year came up with five of the most common pitfalls to cloud migrations.

Not understanding workload needs and insufficient application assessment. Existing workloads might behave unpredictably in a new cloud environment. This can lead to performance issues and failed application migrations.

Insufficient implementation and strategy development. Improper implementation or strategy development can lead to downtime, cost overruns, and a mismatch between an organization’s goals and the outcomes from its cloud implementation.

Security and compliance considerations. Insufficient security and compliance considerations can lead to breaches and fines, as well as a loss of customer goodwill, revenue, and data.

Lack of cost optimization and poor resource management. Without a proper understanding of billing, costs, and how to maximize the return on cloud resource spending, cloud costs can fail to align with business objectives.

Skill gaps. Skill gaps can lead to a domino effect of problems, including poorly designed architecture, inefficient resource allocation, security vulnerabilities, and, ultimately, project failure.

Zencore prioritizes an outcome-based approach that focuses on quickly getting hands-ons with our clients. We want the strategy and architecture to be well thought out, but you cannot spend your time in endless workshops run by consultants. These five pillars best describe our overall methodology.

A deep understanding of the cloud platform. We know Google Cloud inside and out, including key areas like data cloud, machine learning, AI, and Kubernetes.

Proven methodologies. Our streamlined assessment, planning, and migration processes minimize unplanned downtime and reduce the impact on your staff.

The ability to guide the selection of the right intial cloud project tailored for success. We guide you in selecting and planning cloud projects that are set up for success, especially during early phases like evaluating workload migrations.

Expertise in cloud security. We help minimize risks with our deep knowledge of cloud security, protecting you from data breaches and other costly issues.

Hands on development capabilities. We are outcome oriented, and bring the engineering resources needed to get your solution deployed and running in production

With the cloud technology landscape continuously evolving, what emerging trends do you believe will significantly impact how organizations adopt and utilize Google Cloud in the next few years?

I think we are on a journey here in the constantly evolving cloud space. I’ll describe it in 3 steps, and I believe we’re somewhere in between step 2 and 3.

First, we all experienced the shift from Infrastructure as a Service (IaaS) to Platform as a Service (PaaS). Companies are increasingly favoring PaaS solutions because they simplify the development process, reduce the need for managing underlying infrastructure, and accelerate time-to-market. Google Cloud’s PaaS offerings, such as Cloud Run, allow developers to focus more on coding and less on maintenance, which fosters innovation and efficiency.

Second, the rise of managed services is transforming the way organizations handle their cloud operations. Managed services like Google Kubernetes Engine (GKE), Cloud SQL and BigQuery take the burden of routine management tasks off the shoulders of IT teams. This shift not only improves operational efficiency but also ensures higher levels of reliability and security. By leveraging these managed services, organizations can allocate more resources towards strategic initiatives rather than routine upkeep.

Lastly, the integration of generative AI is set to revolutionize business operations across various industries. Google Cloud’s AI and machine learning services, including the new generative AI models, empower businesses to harness advanced analytics, automate complex processes, and enhance customer experiences. For example, tools like Vertex AI make it easier for companies to develop and deploy sophisticated AI models, driving innovation and creating new value propositions.

This is just the beginning of the age of AI in everyday life for organizations running on Google Cloud and it’s definitely where we see a lot of momentum. To that end we built a set of services at Zencore we call Zen AI to help companies building AI applications or integrating AI into their existing processes.

How has your background at Google influenced your leadership style at Zencore, and what key qualities do you look for when assembling your team of cloud experts?

It’s a great question. When you look at the SRE organization at Google the Individual Contributors (ICs) are the most important part of the organization, not the managers. The ICs are highly paid, well respected and make things work without a lot of oversight. They are truly the special forces inside of Google.

What I learned is that if you hire the right people things actually work very well without a dedicated people management layer at our size. I think that one of the most unique things about Zencore is that there are no individuals whose only job is to manage people. We are an assembly of ICs who are still pretty good at their area of expertise that lead others who may be a little less experienced. Creating a company of leaders instead of a company of managers has become a key component to the culture we have created. You respect your manager because in most cases he or she is more experienced at their job and still performing it at a very high level. It’s a very collaborative approach.

From an engineering perspective, we have very high standards. We review so many resumes and they all look similar with the standard Google Cloud professional certifications listed. We generally don’t care how many certs you have obtained. What matters to us when we are hiring an architect or engineer is significant practical experience with Google Cloud. Your experience with migrations, ML ops, building a Kubernetes Operator or your depth with complex data environments leveraging BigQuery are what’s meaningful to Zencore and its clients.

Could you share a case study where Zencore’s approach significantly improved a client’s business outcomes through cloud adoption?

Although migration work is a key component of our business, it’s the data platform engagements that really stand out when you’re talking about value to the business.

One project that really stands out is a complex engagement that involved working with a company that was made up of a diverse portfolio of software brands. They were struggling with operational inefficiencies and an incomplete view of their business due to data being siloed across their various brands. This led to inconsistent data standards and made it difficult for them to gain actionable insights.

When Zencore came on board, our primary goal was to consolidate these disparate data sources and build a highly scalable data platform on Google Cloud Platform. We tackled this challenge through several key initiatives:

First, we migrated their various databases, including Redshift and SQL Server, to BigQuery. This step unified their data landscape, making it easier and more efficient for them to access and analyze their data.

Next, we focused on enhancing their data ingestion and validation processes. By implementing and automating their data job orchestration and integrating CI/CD pipelines, we ensured that their data ingestion was reliable and timely. This setup also improved the data validation checks, which are crucial for maintaining data integrity.

We also standardized their data modeling using DBT, which is an open source tool that allows you to develop data transformation models in a version controlled, easy to understand manner. . This allowed a standardization of data models across the many disparate brands, which made data analysis and reporting much easier for their teams across their portfolio.

Additionally, we consolidated multiple BI tools into a single Looker environment on GCP. This move streamlined their reporting processes and provided a unified platform for generating insights across all their portfolio companies.

The impact of these efforts was transformative. Our client now has a consolidated data environment, which gives them a comprehensive view of their business operations. This unified data platform has significantly improved their strategic decision-making capabilities and operational efficiency. Furthermore, this transformation enabled them to develop a new strategy to monetize their data, creating a new revenue stream and providing them with a strategic advantage in the market.

Looking ahead, what are your long-term goals for Zencore, and how do you plan to evolve your services to meet the future needs of your clients?

The market moves so fast that I’m not joking when I say six months is long term. I think the biggest opportunity for both Zencore and Google Cloud is with Generative AI. We have moved quickly past the hype phase and are now working on projects with real operational value that will go into production. And the value of Gen AI is so compelling that it’s putting massive pressure on organizations to get their data house in order to leverage the technology. The risk of not engaging and understanding the value of Gen AI is that your competition will use it to leapfrog you in the market.

So Zencore is doing several things to address this opportunity. One is to continue to invest in the right architects and engineers that have experience across a broad set of industries and use cases focused on things like RAG, enterprise search and of course Google products like Vertex AI.

You will also see us take a much more vertical approach, which is something historically we have not done. When you solve a specific challenge for one client in an industry using Gen AI, the reality is that you have done 80% of the work to solve the challenge for a significant number of clients in the industry. This is a unique advantage for us when time to market is critical.

Finally you will see us make a significant investment in our data cloud practice. Zencore will always have a 360 degree approach to Gen AI projects and be ready to focus on the infrastructure, security, data pipelines and ml ops to ensure a successful end to end production solution.

Thank you for the great interview, readers who wish to learn more should visit Zencore.

[onlinemarktplatz-de]

Podcast: ROPT BI Tool - Amazon Umsatz absichern, erhöhen und hunderte Stunden monatlich sparen

ROPT ist das Movesell eigene BI Analyse Tool mit dem Amazon Vendoren und Seller alle für sie relevanten Amazon Daten im Blick behalten können, um datenbasierte, strategische Entscheidungen treffen und schnell auf sinkende Umsätze, Buybox Verluste oder Content Issues reagieren zu können. Lesen Sie den ganzen Artikel

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April 2023

Sechs Jahre Nichtstun, eine schöne Lösung für so viele Probleme

Vor fast genau sechs Jahren habe ich beschlossen, auch mal dieses Machine Learning auszuprobieren:

Gleich kann es losgehen, ich muss nur erst “Getting Started before your first lesson” lesen. Von dort schickt man mich weiter zum AWS deep learning setup video. Das Video ist 13 Minuten lang.

(Es folgen Probleme und Verwicklungen beim Setup, die Details kann man hier nachlesen.)

In Minute 12:45 sagt der Erzähler im Video: “Ok! It looks like everything is set up correctly and you’re ready to start using it.” Aber statt 12 Minuten und 45 Sekunden sind zwei Wochen vergangen, mein anfänglicher Enthusiasmus ist aufgebraucht und mein Interesse an Deep Learning erlahmt. Ich bin nicht einmal bis “Lesson 1” gekommen.

Im April 2023 sagt Aleks, dass er gerade einen sehr guten Onlinekurs über Machine Learning macht. Ich frage nach der Adresse, und sie kommt mir bekannt vor. Es ist derselbe Kurs!

“Das Setup war kein Problem?”, frage ich. Nein, sagt Aleks, Sache von ein paar Minuten.

Ich sehe mir "Practical Deep Learning for Coders 2022” an. Man braucht für den Kurs bestimmte Hardware. Generell benötigt Machine Learning Grafikprozessoren wegen der höheren Rechenleistung, und aus der Einleitung zum Kurs weiß ich jetzt, dass die aktuell verfügbaren Tools Nvidia-Grafikprozessoren voraussetzen*. Den Zugang zu dieser Hardware soll man mieten. Das war vor sechs Jahren auch schon so, nur dass das Mieten der Rechenleistung bei Amazon Web Services eine komplizierte und teure Sache war.

* Ich hatte an dieser Stelle schon “Grafikkarten” geschrieben, dann kam es mir aber wieder so vor, als müsste ich meinen Sprachgebrauch renovieren. In meiner Vorstellung handelt es sich um eine Steckkarte, ungefähr 10 x 20 cm groß, die in ein PC-Gehäuse eingebaut wird. So war das, als ich meine Computer noch in Einzelteilen kaufte, aber das ist zwanzig Jahre her. Deshalb habe ich mich für das unverbindliche Wort “Grafikprozessoren” entschieden. Aber wenn ich nach nvidia gpu machine learning suche, sehe ich sperrige Dinge, die nicht weit von meiner Erinnerung an Grafikkarten entfernt sind. Die große Rechenleistung braucht auch große Kühlleistung, deshalb sind zwei Lüfter auf der ... naja, Karte. Die Ergebnisse der Bildersuche sind etwas uneindeutig, aber es kommt mir so vor, als enthielte das Rechenzentrum, dessen Leistung ich gleich nutzen werde, wahrscheinlich große Gehäuse, in denen große Grafikkarten drin sind, vom Format her immer noch ungefähr wie vor zwanzig Jahren. Nur viel schneller.

2018 brauchte man AWS schon nicht mehr für den fast.ai-Onlinekurs. Stattdessen konnte man sich die Arbeitsumgebung bei Paperspace einrichten, einem anderen Cloud-Anbieter. Die Anleitung von 2018 klingt so, als hätte meine Geduld wahrscheinlich auch dafür nicht gereicht.

In der Version von 2019 hat der Kurs auf Google Colab gesetzt. Das heißt, dass man Jupyter Notebooks auf Google-Servern laufen lassen kann und keine eigene Python-Installation braucht, nur einen Browser. Colab gab es 2017 noch nicht, es wurde erst ein paar Monate nach meinem Scheitern, im Herbst 2017, für die Öffentlichkeit freigegeben. Allerdings klingt die Anleitung von 2019 immer noch kompliziert.

2020 wirkt es schon schaffbarer.

Auch die aktuelle Version des Kurses basiert auf Colab. Man muss sich dafür einen Account bei Kaggle einrichten. Soweit ich es bisher verstehe, dient dieser Kaggle-Zugang dazu, die Sache kostenlos zu machen. Colab würde ansonsten Geld kosten, weniger als ich 2017 bezahlt habe, aber eben Geld. Oder vielleicht liegen auch die Jupyter Notebooks mit den Kurs-Übungen bei Kaggle, keine Ahnung, man braucht es eben. (Update: In Kapitel 2 des Kurses merke ich, dass es noch mal anders ist, man hätte sich zwischen Colab und Kaggle entscheiden können. Zusammengefasst: Ich verstehe es nicht.)

Ich lege mir einen Kaggle-Account an und betrachte das erste Python-Notebook des Kurses. Es beginnt mit einem Test, der nur überprüft, ob man überhaupt Rechenleistung bei Kaggle in Anspruch nehmen darf. Das geht nämlich erst, wenn man eine Telefonnummer eingetragen und einen Verifikationscode eingetragen hat, der an diese Telefonnummer verschickt wird. Aber das Problem ist Teil des Kursablaufs und deshalb genau an der Stelle erklärt, an der es auftritt. Es kostet mich fünf Minuten, die vor allem im Warten auf die Zustellung der SMS mit dem Code bestehen.

Danach geht es immer noch nicht. Beim Versuch, die ersten Zeilen Code laufen zu lassen, bekomme ich eine Fehlermeldung, die mir sagt, dass ich das Internet einschalten soll:

“STOP: No internet. Click ‘>|’ in top right and set ‘Internet’ switch to on.”

Ich betrachte lange alles, was mit “top right” gemeint sein könnte, aber da ist kein solcher Schalter. Schließlich google ich die Fehlermeldung. Andere haben das Problem auch schon gehabt und gelöst. Der Schalter sieht weder so aus wie in der Fehlermeldung angedeutet, noch befindet er sich oben rechts. Man muss ein paar Menüs ein- und ein anderes ausklappen, dann wird er unten rechts sichtbar.

Ich bin also im Internet und muss erst das Internet einschalten, damit ich Dinge im Internet machen kann.

Aleks meint, wenn ich ihm gestern dabei zugehört hätte, wie er eine Viertelstunde lang laut fluchte, hätte ich schon gewusst, wie es geht. Hatte ich aber nicht.

Nach dem Einschalten des Internets kann ich das erste Jupyter-Notebook des Kurses betrachten und selbst ausprobieren, ob es wohl schwer ist, Frösche von Katzen zu unterscheiden. Für die Lösung aller Startprobleme von 2017 habe ich zwei Wochen gebraucht. 2023 noch eine Viertelstunde, und ich bin zuversichtlich, dass man um 2025 direkt in den Kurs einsteigen können wird.

(Kathrin Passig)