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azurecertifications1 · 9 months

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Navigating the Future as Azure Certifications in 2024

In the ever-evolving landscape of cloud technology, Azure certifications continue to be instrumental in shaping the careers of IT professionals and influencing the strategies of organizations worldwide. As we step into 2024, it's essential to explore the current trends, advancements, and the transformative impact that Azure certifications are poised to have in the coming year.

The Continued Relevance of Azure Certifications

Azure certifications are not mere credentials; they are gateways to expertise and recognition in the dynamic world of cloud computing. As businesses increasingly rely on cloud solutions, the demand for skilled Azure professionals continues to grow. In 2024, Azure certifications stand as key assets, ensuring that professionals possess the knowledge and skills needed to navigate the complexities of cloud technology effectively.

Azure Certification Paths in 2024

Azure certifications are structured into comprehensive paths, catering to individuals at various stages of their cloud journey. From foundational certifications for beginners to advanced tracks tailored for specialized roles like Azure Solutions Architect or Azure DevOps Engineer, the certification paths have evolved to align with the diverse needs of IT professionals. The year 2024 sees an increased focus on role-based certifications, allowing individuals to hone specific skills relevant to their job roles.

Key Benefits of Azure Certifications in 2024

Career Advancement:

Azure certifications are a proven catalyst for career growth. In 2024, as businesses seek skilled professionals, holding an Azure certification becomes a valuable asset for those aiming to advance their careers in cloud technology.

Industry Recognition:

Globally recognized, Azure certifications validate one's expertise in Microsoft's cloud services. Employers across industries acknowledge and value these certifications as a mark of proficiency in handling Azure-based solutions.

Continuous Learning Culture:

In 2024, Azure certifications are not just about achieving a one-time qualification; they embody a commitment to continuous learning. Microsoft regularly updates its certification paths to align with emerging technologies, encouraging professionals to stay abreast of the latest industry trends.

Increased Employability:

As the job market becomes more competitive, possessing Azure certifications enhances employability. In 2024, businesses are actively seeking candidates with practical Azure skills, making certification holders highly sought after.

Emerging Trends in Azure Certifications for 2024

Micro-Certifications:

2024 witnesses a rise in micro-certifications, focusing on specific, targeted skills. These bite-sized certifications allow professionals to demonstrate expertise in niche areas, providing a more granular approach to skill validation.

Scenario-Based Assessments:

Azure certification exams in 2024 are increasingly incorporating scenario-based questions. This shift aims to evaluate not just theoretical knowledge but the ability to apply that knowledge in practical, real-world situations.

Integration of AI and ML:

With the growing importance of artificial intelligence (AI) and machine learning (ML), Azure certifications in 2024 are placing a greater emphasis on these technologies. Certification tracks dedicated to AI and ML applications within Azure are gaining prominence.

Focus on Security:

In response to the heightened concern for cybersecurity, Azure certifications in 2024 place a significant focus on security-related tracks. Azure Security Engineer certifications are expected to be in high demand as organizations prioritize securing their cloud environments.

Tips for Success in Azure Certifications 2024

Stay Updated: Given the evolving nature of technology, staying updated with the latest Azure services and features is crucial. Regularly check Microsoft's official documentation and announcements for any updates.

Hands-On Experience: Practical experience is invaluable. Utilize Azure's sandbox environments, participate in real-world projects, and engage with the Azure portal to reinforce your theoretical knowledge.

Leverage Learning Resources: Microsoft provides a wealth of learning resources, including online courses, documentation, and practice exams. Take advantage of these resources to supplement your preparation.

Join the Azure Community: Engage with the Azure community through forums, webinars, and social media. Networking with professionals in the field can provide insights, tips, and support during your certification journey.

Conclusion

As we venture into 2024, Azure certifications stand as pivotal tools for IT professionals aiming to thrive in the dynamic world of cloud technology. Whether you are starting your journey with foundational certifications or advancing your skills with specialized tracks, Azure certifications in 2024 represent more than just qualifications – they symbolize a commitment to excellence, continuous learning, and a future shaped by innovation in the cloud. Embrace the opportunities, stay ahead of the curve, and let Azure certifications be your guide to success in the ever-evolving realm of cloud proficiency.

Frequently Asked Questions (FAQs)

What are the new Azure certifications introduced in 2024?

As of 2024, Microsoft has introduced several new certifications to align with emerging technologies. Notable additions include specialized tracks focusing on AI, ML, and advanced security.

How has the exam format changed for 2024?

The exam format in 2024 has evolved to include more scenario-based questions. This change is aimed at assessing practical application skills in addition to theoretical knowledge.

Are there any prerequisites for Azure certifications in 2024?

Prerequisites vary based on the specific certification. While some foundational certifications may have no prerequisites, advanced certifications often require prior experience or the completion of specific lower-level certifications.

Can I still take exams for older Azure certifications in 2024?

Microsoft often provides a transition period for older certifications, allowing candidates to complete them even as new certifications are introduced. However, it's advisable to check Microsoft's official documentation for specific details.

How frequently are Azure certifications updated?

Azure certifications are regularly updated to stay aligned with the latest technologies and industry trends. Microsoft recommends that candidates stay informed about updates through official communication channels.

#azure training #azure certification #microsoft azure #azure developer #azure cloud services

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gradsireninc · 1 year

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How To Get An Online Internship In the IT Sector (Skills And Tips)

Internships provide invaluable opportunities to gain practical skills, build professional networks, and get your foot in the door with top tech companies.

With remote tech internships exploding in IT, online internships are now more accessible than ever. Whether a college student or career changer seeking hands-on IT experience, virtual internships allow you to work from anywhere.

However, competition can be fierce, and simply applying is often insufficient. Follow this comprehensive guide to develop the right technical abilities.

After reading this, you can effectively showcase your potential, and maximize your chances of securing a remote tech internship.

Understand In-Demand IT Skills

The first step is gaining a solid grasp of the most in-demand technical and soft skills. While specific requirements vary by company and role, these competencies form a strong foundation:

Technical Skills:

Proficiency in programming languages like Python, JavaScript, Java, and C++

Experience with front-end frameworks like React, Angular, and Vue.js

Back-end development skills - APIs, microservices, SQL databases Cloud platforms such as AWS, Azure, Google Cloud

IT infrastructure skills - servers, networks, security

Data science abilities like SQL, R, Python

Web development and design

Mobile app development - Android, iOS, hybrid

Soft Skills:

Communication and collaboration

Analytical thinking and problem-solving

Leadership and teamwork

Creativity and innovation

Fast learning ability

Detail and deadline-oriented

Flexibility and adaptability

Obtain Relevant Credentials

While hands-on skills hold more weight, relevant academic credentials and professional IT certifications can strengthen your profile. Consider pursuing:

Bachelor’s degree in Computer Science, IT, or related engineering fields

Internship-specific courses teaching technical and soft skills

Certificates like CompTIA, AWS, Cisco, Microsoft, Google, etc.

Accredited boot camp programs focusing on applied skills

MOOCs to build expertise in trending technologies like AI/ML, cybersecurity

Open source contributions on GitHub to demonstrate coding skills

The right credentials display a work ethic and supplement practical abilities gained through projects.

Build An Impressive Project Portfolio

Nothing showcases skills better than real-world examples of your work. Develop a portfolio of strong coding, design, and analytical projects related to your target internship field.

Mobile apps - publish on app stores or use GitHub project pages

Websites - deploy online via hosting services

Data science - showcase Jupyter notebooks, visualizations

Open source code - contribute to public projects on GitHub

Technical writing - blog posts explaining key concepts

Automation and scripts - record demo videos

Choose projects demonstrating both breadth and depth. Align them to skills required for your desired internship roles.

Master Technical Interview Skills

IT internship interviews often include challenging technical questions and assessments. Be prepared to:

Explain your code and projects clearly. Review them beforehand.

Discuss concepts related to key technologies on your resume. Ramp up on fundamentals.

Solve coding challenges focused on algorithms, data structures, etc. Practice online judges like LeetCode.

Address system design and analytical problems. Read case interview guides.

Show communication and collaboration skills through pair programming tests.

Ask smart, well-researched questions about the company’s tech stack, projects, etc.

Schedule dedicated time for technical interview practice daily. Learn to think aloud while coding and get feedback from peers.

Show Passion and Curiosity

Beyond raw skills, demonstrating genuine passion and curiosity for technology goes a long way.

Take online courses and certifications beyond the college curriculum

Build side projects and engage in hackathons for self-learning

Stay updated on industry news, trends, and innovations

Be active on forums like StackOverflow to exchange knowledge

Attend tech events and conferences

Participate in groups like coding clubs and prior internship programs

Follow tech leaders on social mediaListen to tech podcasts while commuting

Show interest in the company’s mission, products, and culture

This passion shines through in interviews and applications, distinguishing you from other candidates.

Promote Your Personal Brand

In the digital age, your online presence and personal brand are make-or-break. Craft a strong brand image across:

LinkedIn profile - showcase achievements, skills, recommendations

GitHub - displays coding activity and quality through clean repositories

Portfolio website - highlight projects and share valuable content

Social media - post career updates and useful insights, but avoid oversharing

Blogs/videos - demonstrate communication abilities and thought leadership

Online communities - actively engage and build relationships

Ensure your profiles are professional and consistent. Let your technical abilities and potential speak for themselves.

Optimize Your Internship Applications

Applying isn’t enough. You must optimize your internship applications to get a reply:

Ensure you apply to openings that strongly match your profile Customize your resume and cover letters using keywords in the job description

Speak to skills gained from coursework, online learning, and personal projects

Quantify achievements rather than just listing responsibilities

Emphasize passion for technology and fast learning abilities

Ask insightful questions that show business understanding

Follow up respectfully if you don’t hear back in 1-2 weeks

Show interest in full-time conversion early and often

Apply early since competitive openings close quickly

Leverage referrals from your network if possible

This is how you do apply meaningfully. If you want a good internship, focus on the quality of applications. The hard work will pay off.

Succeed in Your Remote Internship

The hard work pays off when you secure that long-awaited internship! Continue standing out through the actual internship by:

Over Communicating in remote settings - proactively collaborate

Asking smart questions and owning your learning

Finding mentors and building connections remotely

Absorbing constructive criticism with maturity

Shipping quality work on or before deadlines

Clarifying expectations frequently

Going above and beyond prescribed responsibilities sometimes

Getting regular feedback and asking for more work

Leaving with letters of recommendation and job referrals

When you follow these tips, you are sure to succeed in your remote internship. Remember, soft skills can get you long ahead in the company, sometimes core skills can’t.

Conclusion

With careful preparation, tenacity, and a passion for technology, you will be able to get internships jobs in USA that suit your needs in the thriving IT sector.

Use this guide to build the right skills, create an impressive personal brand, ace the applications, and excel in your internship.

Additionally, you can browse some good job portals. For instance, GrandSiren can help you get remote tech internships. The portal has the best internship jobs in India and USA you’ll find. The investment will pay dividends throughout your career in this digital age. Wishing you the best of luck! Let me know in the comments about your internship hunt journey.

#itjobs #internship opportunities #internships #interns #entryleveljobs #gradsiren #opportunities #jobsearch #careeropportunities #jobseekers #ineffable interns #jobs #employment #career

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gradsiren · 1 year

Text

How To Get An Online Internship In the IT Sector (Skills And Tips)

Internships provide invaluable opportunities to gain practical skills, build professional networks, and get your foot in the door with top tech companies.

However, competition can be fierce, and simply applying is often insufficient. Follow this comprehensive guide to develop the right technical abilities.

After reading this, you can effectively showcase your potential, and maximize your chances of securing a remote tech internship.

Understand In-Demand IT Skills

The first step is gaining a solid grasp of the most in-demand technical and soft skills. While specific requirements vary by company and role, these competencies form a strong foundation:

Technical Skills:

>> Proficiency in programming languages like Python, JavaScript, Java, and C++ >> Experience with front-end frameworks like React, Angular, and Vue.js >> Back-end development skills - APIs, microservices, SQL databases >> Cloud platforms such as AWS, Azure, Google Cloud >> IT infrastructure skills - servers, networks, security >> Data science abilities like SQL, R, Python >> Web development and design >> Mobile app development - Android, iOS, hybrid

Soft Skills:

>> Communication and collaboration >> Analytical thinking and problem-solving >> Leadership and teamwork >> Creativity and innovation >> Fast learning ability >> Detail and deadline-oriented >> Flexibility and adaptability

Obtain Relevant Credentials

While hands-on skills hold more weight, relevant academic credentials and professional IT certifications can strengthen your profile. Consider pursuing:

>> Bachelor’s degree in Computer Science, IT, or related engineering fields. >> Internship-specific courses teaching technical and soft skills. >> Certificates like CompTIA, AWS, Cisco, Microsoft, Google, etc. >> Accredited boot camp programs focusing on applied skills. >> MOOCs to build expertise in trending technologies like AI/ML, cybersecurity. >> Open source contributions on GitHub to demonstrate coding skills.

The right credentials display a work ethic and supplement practical abilities gained through projects.

Build An Impressive Project Portfolio

Nothing showcases skills better than real-world examples of your work. Develop a portfolio of strong coding, design, and analytical projects related to your target internship field.

>> Mobile apps - publish on app stores or use GitHub project pages >> Websites - deploy online via hosting services >> Data science - showcase Jupyter notebooks, visualizations >> Open source code - contribute to public projects on GitHub >> Technical writing - blog posts explaining key concepts >> Automation and scripts - record demo videos

Choose projects demonstrating both breadth and depth. Align them to skills required for your desired internship roles.

Master Technical Interview Skills

IT internship interviews often include challenging technical questions and assessments. Be prepared to:

>> Explain your code and projects clearly. Review them beforehand. >> Discuss concepts related to key technologies on your resume. Ramp up on fundamentals. >> Solve coding challenges focused on algorithms, data structures, etc. Practice online judges like LeetCode. >> Address system design and analytical problems. Read case interview guides. >> Show communication and collaboration skills through pair programming tests. >> Ask smart, well-researched questions about the company’s tech stack, projects, etc.

Schedule dedicated time for technical interview practice daily. Learn to think aloud while coding and get feedback from peers.

Show Passion and Curiosity

Beyond raw skills, demonstrating genuine passion and curiosity for technology goes a long way.

>> Take online courses and certifications beyond the college curriculum >> Build side projects and engage in hackathons for self-learning >> Stay updated on industry news, trends, and innovations >> Be active on forums like StackOverflow to exchange knowledge >> Attend tech events and conferences >> Participate in groups like coding clubs and prior internship programs >> Follow tech leaders on social media >> Listen to tech podcasts while commuting >> Show interest in the company’s mission, products, and culture

This passion shines through in interviews and applications, distinguishing you from other candidates.

Promote Your Personal Brand

In the digital age, your online presence and personal brand are make-or-break. Craft a strong brand image across:

>> LinkedIn profile - showcase achievements, skills, recommendations >> GitHub - displays coding activity and quality through clean repositories >> Portfolio website - highlight projects and share valuable content >> Social media - post career updates and useful insights, but avoid oversharing >> Blogs/videos - demonstrate communication abilities and thought leadership >> Online communities - actively engage and build relationships

Ensure your profiles are professional and consistent. Let your technical abilities and potential speak for themselves.

Optimize Your Internship Applications

Applying isn’t enough. You must optimize your internship applications to get a reply:

>> Ensure you apply to openings that strongly match your profile >> Customize your resume and cover letters using keywords in the job description >> Speak to skills gained from coursework, online learning, and personal projects >> Quantify achievements rather than just listing responsibilities >> Emphasize passion for technology and fast learning abilities >> Ask insightful questions that show business understanding >> Follow up respectfully if you don’t hear back in 1-2 weeks >> Show interest in full-time conversion early and often >> Apply early since competitive openings close quickly >> Leverage referrals from your network if possible

This is how you do apply meaningfully. If you want a good internship, focus on the quality of applications. The hard work will pay off.

Succeed in Your Remote Internship

The hard work pays off when you secure that long-awaited internship! Continue standing out through the actual internship by:

>> Over Communicating in remote settings - proactively collaborate >> Asking smart questions and owning your learning >> Finding mentors and building connections remotely >> Absorbing constructive criticism with maturity >> Shipping quality work on or before deadlines >> Clarifying expectations frequently >> Going above and beyond prescribed responsibilities sometimes >> Getting regular feedback and asking for more work >> Leaving with letters of recommendation and job referrals

When you follow these tips, you are sure to succeed in your remote internship. Remember, soft skills can get you long ahead in the company, sometimes core skills can’t.

Conclusion

With careful preparation, tenacity, and a passion for technology, you will be able to get internships jobs in USA that suit your needs in the thriving IT sector.

Use this guide to build the right skills, create an impressive personal brand, ace the applications, and excel in your internship.

Additionally, you can browse some good job portals. For instance, GrandSiren can help you get remote tech internships. The portal has the best internship jobs in India and USA you’ll find.

The investment will pay dividends throughout your career in this digital age. Wishing you the best of luck! Let me know in the comments about your internship hunt journey.

#internship #internshipopportunity #it job opportunities #it jobs #IT internships #jobseekers #jobsearch #entryleveljobs #employment #gradsiren #graduation #computer science #technology #engineering #innovation #information technology #remote jobs #remote work #IT Remote jobs

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greenoperator · 1 year

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Microsoft Azure Fundamentals AI-900 (Part 5)

Microsoft Azure AI Fundamentals: Explore visual studio tools for machine learning

What is machine learning? A technique that uses math and statistics to create models that predict unknown values

Types of Machine learning

Regression - predict a continuous value, like a price, a sales total, a measure, etc

Classification - determine a class label.

Clustering - determine labels by grouping similar information into label groups

x = features

y = label

Azure Machine Learning Studio

You can use the workspace to develop solutions with the Azure ML service on the web portal or with developer tools

Web portal for ML solutions in Sure

Capabilities for preparing data, training models, publishing and monitoring a service.

First step assign a workspace to a studio.

Compute targets are cloud-based resources which can run model training and data exploration processes

Compute Instances - Development workstations that data scientists can use to work with data and models

Compute Clusters - Scalable clusters of VMs for on demand processing of experiment code

Inference Clusters - Deployment targets for predictive services that use your trained models

Attached Compute - Links to existing Azure compute resources like VMs or Azure data brick clusters

What is Azure Automated Machine Learning

Jobs have multiple settings

Provide information needed to specify your training scripts, compute target and Azure ML environment and run a training job

Understand the AutoML Process

ML model must be trained with existing data

Data scientists spend lots of time pre-processing and selecting data

This is time consuming and often makes inefficient use of expensive compute hardware

In Azure ML data for model training and other operations are encapsulated in a data set.

You create your own dataset.

Classification (predicting categories or classes)

Regression (predicting numeric values)

Time series forecasting (predicting numeric values at a future point in time)

After part of the data is used to train a model, then the rest of the data is used to iteratively test or cross validate the model

The metric is calculated by comparing the actual known label or value with the predicted one

Difference between the actual known and predicted is known as residuals; they indicate amount of error in the model.

Root Mean Squared Error (RMSE) is a performance metric. The smaller the value, the more accurate the model’s prediction is

Normalized root mean squared error (NRMSE) standardizes the metric to be used between models which have different scales.

Shows the frequency of residual value ranges.

Residuals represents variance between predicted and true values that can’t be explained by the model, errors

Most frequently occurring residual values (errors) should be clustered around zero.

You want small errors with fewer errors at the extreme ends of the sale

Should show a diagonal trend where the predicted value correlates closely with the true value

Dotted line shows a perfect model’s performance

The closer to the line of your model’s average predicted value to the dotted, the better.

Services can be deployed as an Azure Container Instance (ACI) or to a Azure Kubernetes Service (AKS) cluster

For production AKS is recommended.

Identify regression machine learning scenarios

Regression is a form of ML

Understands the relationships between variables to predict a desired outcome

Predicts a numeric label or outcome base on variables (features)

Regression is an example of supervised ML

What is Azure Machine Learning designer

Allow you to organize, manage, and reuse complex ML workflows across projects and users

Pipelines start with the dataset you want to use to train the model

Each time you run a pipelines, the context(history) is stored as a pipeline job

Encapsulates one step in a machine learning pipeline.

Like a function in programming

In a pipeline project, you access data assets and components from the Asset Library tab

You can create data assets on the data tab from local files, web files, open at a sets, and a datastore

Data assets appear in the Asset Library

Azure ML job executes a task against a specified compute target.

Jobs allow systematic tracking of your ML experiments and workflows.

Understand steps for regression

To train a regression model, your data set needs to include historic features and known label values.

Use the designer’s Score Model component to generate the predicted class label value

Connect all the components that will run in the experiment

Average difference between predicted and true values

It is based on the same unit as the label

The lower the value is the better the model is predicting

The square root of the mean squared difference between predicted and true values

Metric based on the same unit as the label.

A larger difference indicates greater variance in the individual label errors

Relative metric between 0 and 1 on the square based on the square of the differences between predicted and true values

Closer to 0 means the better the model is performing.

Since the value is relative, it can compare different models with different label units

Relative metric between 0 and 1 on the square based on the absolute of the differences between predicted and true values

Closer to 0 means the better the model is performing.

Can be used to compare models where the labels are in different units

Also known as R-squared

Summarizes how much variance exists between predicted and true values

Closer to 1 means the model is performing better

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

Create a classification model with Azure ML designer

Classification is a form of ML used to predict which category an item belongs to

Like regression this is a supervised ML technique.

Understand steps for classification

True Positive - Model predicts the label and the label is correct

False Positive - Model predicts wrong label and the data has the label

False Negative - Model predicts the wrong label, and the data does have the label

True Negative - Model predicts the label correctly and the data has the label

For multi-class classification, same approach is used. A model with 3 possible results would have a 3x3 matrix.

Diagonal lien of cells were the predicted and actual labels match

Number of cases classified as positive that are actually positive

True positives divided by (true positives + false positives)

Fraction of positive cases correctly identified

Number of true positives divided by (true positives + false negatives)

Overall metric that essentially combines precision and recall

Classification models predict probability for each possible class

For binary classification models, the probability is between 0 and 1

Setting the threshold can define when a value is interpreted as 0 or 1. If its set to 0.5 then 0.5-1.0 is 1 and 0.0-0.4 is 0

Recall also known as True Positive Rate

Has a corresponding False Positive Rate

Plotting these two metrics on a graph for all values between 0 and 1 provides information.

Receiver Operating Characteristic (ROC) is the curve.

In a perfect model, this curve would be high to the top left

Area under the curve (AUC).

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

Create a Clustering model with Azure ML designer

Clustering is used to group similar objects together based on features.

Clustering is an example of unsupervised learning, you train a model to just separate items based on their features.

Understanding steps for clustering

Prebuilt components exist that allow you to clean the data, normalize it, join tables and more

Requires a dataset that includes multiple observations of the items you want to cluster

Requires numeric features that can be used to determine similarities between individual cases

Initializing K coordinates as randomly selected points called centroids in an n-dimensional space (n is the number of dimensions in the feature vectors)

Plotting feature vectors as points in the same space and assigns a value how close they are to the closes centroid

Moving the centroids to the middle points allocated to it (mean distance)

Reassigning to the closes centroids after the move

Repeating the last two steps until tone.

Maximum distances between each point and the centroid of that point’s cluster.

If the value is high it can mean that cluster is widely dispersed.

With the Average Distance to Closer Center, we can determine how spread out the cluster is

Remove training components form your data and replace it with a web service inputs and outputs to handle the web requests

It does the same data transformations as the first pipeline for new data

It then uses trained model to infer/predict label values based on the features.

#microsoft #AI-900 #AI Fundamentals #AI/ML

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cloudatlasinc · 2 years

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Accelerating transformation with SAP on Azure

Microsoft continues to expand its presence in the cloud by building more data centers globally, with over 61 Azure regions in 140 countries. They are expanding their reach and capabilities to meet all the customer needs. The transition from a cloudless domain like DRDC to the entire cloud platform is possible within no time, and a serverless future awaits. Microsoft gives the platform to build and innovate at a rapid speed. Microsoft is enhancing new capabilities to meet cloud services' demands and needs, from IaaS to PaaS Data, AI, ML, and IoT. There are over 600 services available on Azure with a cloud adoption framework and enterprise-scale landing zone. Many companies look at Microsoft Azure security compliance as a significant migration driver. Microsoft Azure has an extensive list of compliance certifications across the globe. The Microsoft services have several beneficial characteristics; capabilities are broad, deep, and suited to any industry, along with a global network of skilled professionals and partners. Expertise in the Microsoft portfolio includes both technology integration and digital transformation. Accountability for the long term, addressing complex challenges while mitigating risk. Flexibility to engage in the way that works for you with the global reach to satisfy the target business audience.

SAP and Microsoft Azure

SAP and Microsoft bring together the power of industry-specific best practices, reference architectures, and professional services and support to simplify and safeguard your migration to SAP in the cloud and help manage the ongoing business operations now and in the future. SAP and Microsoft have collaborated to design and deliver a seamless, optimized experience to help manage migration and business operations as you move from on-premises editions of SAP solutions to SAP S/4 HANA on Microsoft Azure. It reduces complexity, minimizes costs, and supports end-to-end SAP migration and operations strategy, platform, and services. As a result, one can safeguard the cloud migration with out-of-box functionality and industry-specific best practices while immaculately handling the risk and optimizing the IT environment. Furthermore, the migration assimilates best-in-class technologies from SAP and Microsoft, packed with a unified business cloud platform.

SAP Deployment Options on Azure

SAP system is deployed on-premises or in Azure. One can deploy different systems into different landscapes either on Azure or on-premises. SAP HANA on Azure large instances intend to host the SAP application layer of SAP systems in Virtual Machines and the related SAP HANA instance on the unit in the 'SAP HANA Azure Large Instance Stamp.' 'A Large Instance Stamp' is a hardware infrastructure stack that is SAP HANA TDI certified and dedicated to running SAP HANA instances within Azure. 'SAP HANA Large Instances' is the official name for the solution in Azure to run HANA instances on SAP HANA TDI certified hardware that gets deployed in ‘Large Instance Stamps’ in different Azure regions. SAP or HANA Large Instances or HLI are physical servers meaning bare metal servers. HLI does not reside in the same data center as Azure services but is in close proximity and connected through high throughput links to satisfy SAP HANA network latency requirements. HLI comes in two flavors- Type 1 and 2. IaaS can install SAP HANA on a virtual machine running on Azure. Running SAP HANA on IaaS supports more Linux versions than HLI. For example, you can install SAP Netweaver on Windows and Linux IaaS Virtual Machines on Azure. SAP HANA can only run on RedHat and SUSE, while NetWeaver can run on windows SQL and Linux.

Azure Virtual Network

Azure Virtual Network or VNET is a core foundation of the infrastructure implementation on Azure. The VNET can be a communication boundary for those resources that need to communicate. You can have multiple VNETs in your subscription. If they weren't connected, we could call them Pierre in Azure wall; there will be no traffic flow in between. They can also share the same IP range. Understanding the requirements and proper setup is essential as changing them later, especially with the running production workloads, could cause downtime. When you provision a VNET, The private blocks must allocate address space. If you plan to connect multiple VNETs, you cannot have an overlapping address space. The IP range should not clash or overlap with the IP addressing in Azure while connecting on-premise to Azure via express route or site-site VPN. Configuring VNET to the IP address space becomes a DHP service. You can configure VNET with the DNS server's IP addresses to resolve services on-premise.VNETS can be split into different subnets and communicate freely with each other. Network security groups or NSGs are the control planes we use to filter traffic. NSGs are stateful but simple firewall rules based on the source and destination IP and ports.

Azure Virtual Gateway

For extensive connectivity, you must create a virtual gateway subnet. When you create a virtual gateway, you will get prompted for two options: VPN or Express Route Gateway; with VPN, you cannot connect to the Express Route Circuit. If you choose the Express Route Virtual Gateway, you can combine both.

There are two types of VPN;

1) The point-to-site VPN is used for testing and gives the lowest throughput.

2) The site-site VPN connection can offer better benefits by bridging networks.

This VPN offers zero support for SLA and uses this connection as a backup for the recommended connection on Azure, called the express route. Express route is a dedicated circuit using hardware installed on your data center, with a constant link to ‘Microsoft Azure Edge’ devices. Express route is inevitable for maintaining the communication between application VNET running in Azure and on-premise systems to HLI servers. The express route is safer and more resilient than VPN as it provides a connection through a single circuit and facilitates second redundancy; this helps route traffic between SAP application servers inside Azure and enables low latency. Furthermore, the fast path allows routine traffic between SAP application servers inside Azure VNET and HLI through an optimized route that bypasses the virtual network gateway and directly hops through edge routers to HLA servers. Therefore, an ultra-performance express route gateway must have a Fast Path feature.

SAP HANA Architecture (VM)

This design gets centered on the SAP HANA backend on the Linux Suse or RedHat distributions. Even though the Linux OS implementation is the same, the vendor licensing differs. It incorporates always-on replication and utilizes synchronous and asynchronous replication to meet the HANA DB requirements. We have also introduced NetApp file share for DFS volumes used by each SAP component using Azure site recovery and building a DR plan for App ASCs and the web dispatches servers. Azure Active directory is used in synchronization with on-premises active directory, as SAP application user authenticates from on-premises to SAP landscape on Azure with Single Sign-On credentials. Azure high-speed express route gateway securely connects on-premises networks to Azure virtual machines and other resources. The request flows into highly available SAP central, SAP ABAP services ASCS and through SAP application servers running on Azure virtual machines. The on-demand request moves from the SAP App server to the SAP HANA server running on a high-performance Azure VM. Primary active and secondary standby servers run on SAP-certified virtual machines with a cluster availability of 99.95 at the OS level. Data replication is handled through HSR in synchronous mode from primary to secondary enabling zero recovery point objective. SAP HANA data is replicated through a disaster recovery VM in another Azure region through the Azure high-speed backbone network and using HSR in asynchronous mode. The disaster recovery VM can be smaller than the production VM to save costs.

SAP systems are network sensitive, so the network system must factor the design decisions into segmenting the VNETs and NSGs. To ensure network reliability, we must use low latency cross-connections with sufficient bandwidth and no packet loss. SAP is very sensitive to these metrics, and you could experience significant issues if traffic suffers latency or packet loss between the application and the SAP system. We can use proximity placement groups called PGS to force the grouping of different VM types into a single Azure data center to optimize the network latency between the different VM types to the best possible.

Security Considerations

Security is another core pillar of any design. Role-based Access control (RBAC) gets accessed through the Azure management bay. RBAC is backed up through AD using cloud-only synchronized identities. Azure AD can back up the RBAC through cloud-only or synchronized identities. RBAC will tie in those cloud or sync identities to Azure tenants, where you can give personal access to Azure for operational purposes. Network security groups are vital for securing the network traffic both within and outside the network environment. The NSGs are stateful firewalls that preserve session information. You can have a single NSG per subnet, and multiple subnets can share the same energy. Application security group or ASG handles functions such as web servers, application servers, or backend database servers combined to perform a meaningful service. Resource encryption brings the best of security with encryption in transit. SAP recommends using encryption at rest, so for the Azure storage account, we can use storage service encryption, which would use either Microsoft or customer-managed keys to manage encryption. Azure storage also adds encryption in transit, with SSL using HTTPS traffic. You can use Azure Disk Encryption (ADE) for OS and DBA encryption for SQL.

Migration of SAP Workloads to Azure

The most critical part of the migration is understanding what you are planning to migrate and accounting for dependencies, limitations, or even blockers that might stop your migration. Following an appropriate inventory process will ensure that your migration completes successfully. You can use in-hand tools to understand the current SAP landscape in the migration scope. For example, looking at your service now or CMDB catalog might reveal some of the data that expresses your SAP system. Then take that information to start drawing out your sizing in Azure. It is essential to ensure that we have a record of the current environment configuration, such as the number of servers and their names, server roles, and data about CPU and memory. It is essential to pick up the disk sizes, configuration, and throughput to ensure that your design delivers a better experience in Azure. It is also necessary to understand database replication and throughput requirements around replicas. When performing a migration, the sizing for large HANA instances is no different from sizing for HANA in general. For existing and deployment systems you want to move from other RDBMS to HANA, SAP provides several reports that run on your existing SAP systems. If migrating the database to HANA, these reports need to check the data and calculate memory requirements for the HANA instances.

When evaluating high availability and disaster recovery requirements, it is essential to consider the implications of choosing between two-tier and three-tier architectures. To avoid network contention in a two-tier arrangement, install database and Netweaver components on the same Azure VM. The database and application components get installed in three-tier configurations on separate Azure Virtual Machines. This choice has other implications regarding sizing since two-tier, and three-tier SAP ratings for a given VM differs. The high availability option is not mandatory for the SAP application servers.

You can achieve high availability by employing redundancy. To implement it, you can install individual application servers on separate Azure VMs. For example, you can achieve high availability for ASCS and SCS servers running on windows using windows failover clustering with SIOS data keeper. We can also achieve high availability with Linux clustering using Azure NetApp files. For DBMS servers, you should use DB replication technology using redundant nodes. Azure offers high availability through redundancy of its infrastructure and capabilities, such as Azure VM restarts, which play an essential role in a single VM deployment. In addition, Azure offers different SLAs depending on your configuration. For example, SAP landscapes organize SABC servers into different tiers; there are three diverse landscapes: deployment, quality assurance, and production.

Migration Strategies:- SAP landscapes to Azure

Enterprises have SAP systems for business functions like Enterprise Resource Planning(ERP), global trade, business intelligence(BI), and others. Within those systems, there are different environments like sandbox developments, tests, and production. Each horizontal row is an environment, and each vertical dimension is the SAP system for a business function. The layers at the bottom are lower-risk environments and are less critical. Those towards the top are in high-risk environments and are more critical. As you move up the stack, there is more risk in the migration process. Production is the more critical environment. The use of test environments for business continuity is of concern. The systems at the bottom are smaller and have fewer computing resources, lower availability, size requirements, and less throughput. They have the same amount of storage as the production database with a horizontal migration strategy. To gain experience with production systems on Azure, you can use a vertical approach with low-risk factors in parallel to the horizontal design.

Horizontal Migration Strategy

To limit risk, start with low-impact sandboxes or training systems. Then, if something goes wrong, there is little danger associated with users or mission-critical business functions. After gaining experience in hosting, running, and administering SAP systems in Azure, apply to the next layer of systems up the stack. Then, estimate costs, limiting expenditures, performance, and optimization potential for each layer and adjust if needed.

Vertical Migration Strategy

The cost must be on guard along with legal requirements. Move systems from the sandbox to production with the lowest risk. First, the governance, risk, compliance system, and the object Event Repository gets driven towards production. Then the higher risk elements like BI and DRP. When you have a new system, it's better to start in Azure default mode rather than putting it on-premises and moving it later. The last system you move is the highest risk, mission-critical system, usually the ERP production system. Having the most performance virtual machines, SQL, and extensive storage would be best. Consider the earliest migration of standalone systems. If you have different SAP systems, always look for upstream and downstream dependencies from one SAP system to another.

Journey to SAP on Azure

Consider two main factors for the migration of SAP HANA to the cloud. The first is the end-of-life first-generation HANA appliance, causing customers to reevaluate their platform. The second is the desire to take advantage of the early value proposition of SAP business warehouse BW on HANA in a flexible DDA model over traditional databases and later BW for HANA. As a result, numerous initial migrations of SAP HANA to Microsoft Azure have focused on SAP BW to take advantage of SAP HANA's in-memory capability for the BW workloads. In addition, using the SAP database migration option DMO with the System Migration option of SUM facilitates single-step migration from the source system on-premises to the target system residing in Azure. As a result, it minimizes the overall downtime. In general, when initiating a project to deploy SAP workloads to Azure, you should divide it into the following phases. Project preparation and planning, pilot, non-production, production preparation, go-live, and post-production.

Use Cases for SAP Implementation in Microsoft Azure

Use cases

How does Microsoft Azure help?

How do organizations benefit?

Deliver automated disaster recovery with low RPO and RTO

Azure recovery services replicate on-premises virtual machines to Azure and orchestrate failover and failback

RPO and RTO get reduced, and the cost of ownership of disaster recovery (DR) infrastructure diminishes. While the DR systems replicate, the only cost incurred is storage

Make timely changes to SAP workloads by development teams

200-300 times faster infrastructure provisioning and rollout compared to on-premises, more rapid changes by SAP application teams

Increased agility and the ability to provision instances within 20 minutes

Fund intermittently used development and test infrastructure for SAP workloads

Supports the potential to stop development and test systems at the end of business day

Savings as much as 40-75 percent in hosting costs by exercising the ability to control instances when not in use

Increase data center capacity to serve updated SAP project requests

Frees on-premises data center capacity by moving development and test for SAP workloads to Microsoft Azure without upfront investments

Flexibility to shift from capital to operational expenditures

Provide consistent training environments based on templates

Ability to store and use pre-defined images of the training environment for updated virtual machines

Cost savings by provisioning only the instances needed for training and then deleting them when the event is complete

Archive historical systems for auditing and governance

Supports migration of physical machines to virtual machines that get activated when needed

Savings of as much as 60 percent due to cheaper storage and the ability to quickly spin up systems based on need.

References

n.d. Microsoft Azure: Cloud Computing Services. Accessed June 13, 2022. http://azure.microsoft.com.

n.d. All Blog Posts. Accessed June 13, 2022. https://blogs.sap.com.

n.d. Cloud4C: Managed Cloud Services for Enterprises. Accessed June 13, 2022. https://www.cloud4c.com.

n.d. NetApp Cloud Solutions | Optimized Storage In Any Cloud. Accessed June 13, 2022. http://cloud.netapp.com.

#Azure #SAP #CloudMigration #App Modernization #Cloud Computing #Cloud Consulting #Azure Migration

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aishwaryaanair · 4 days

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Top 10 AI Certifications

Top 10 AI Certifications for 2024: Propel Your Career in Artificial Intelligence

Artificial Intelligence (AI) is transforming industries, from healthcare and finance to marketing and technology. As the demand for AI skills grows, obtaining a certification can be a game-changer for professionals looking to excel in this field. Here’s a rundown of the top 10 AI certifications for 2024, each offering unique skills and industry relevance. These certifications come from prestigious organizations like Microsoft, Cisco, AWS, Google, Adobe, IBM, and AI CERTs, placing you at the forefront of AI innovation.

Top 10 AI Certifications:

1. Microsoft Certified: Azure AI Engineer Associate

Introduction:

The Microsoft Certified: Azure AI Engineer Associate is designed for professionals who want to build, manage, and deploy AI solutions using Azure AI services. It covers a range of AI and machine learning tools, making it suitable for those seeking a comprehensive understanding of Azure’s AI capabilities.

Who Will Benefit:

AI engineers, data scientists, and developers looking to integrate AI into cloud-based applications.

Skills to Learn:

Implementing AI workloads on Azure

Integrating AI models into solutions

Natural Language Processing (NLP) and computer vision

Duration:

This certification requires around 30–40 hours of study and practical experience, followed by an exam.

More info on Azure AI Engineer Associate

2. AWS Certified Machine Learning — Specialty

Introduction:

AWS Certified Machine Learning — Specialty is an advanced certification that validates expertise in building, training, and deploying machine learning models on AWS. It’s ideal for professionals who want to specialize in machine learning within the AWS ecosystem.

Who Will Benefit:

Machine learning engineers, data scientists, and developers working on AI projects using AWS services.

Skills to Learn:

Building, training, and deploying ML models on AWS

Implementing scalable ML solutions

Deep learning and advanced data analytics

Duration:

The certification requires approximately 40–50 hours of study, along with hands-on experience, followed by an exam.

More info on AWS Machine Learning Specialty

3. AI+ Data™ — AI CERTs

Introduction:

AI+ Data™ by AI CERTs equips professionals with essential skills in data science and AI, focusing on practical applications in real-world scenarios. The course covers foundational to advanced topics, including data storytelling and machine learning, making it a comprehensive certification for AI enthusiasts.

Who Will Benefit:

Data scientists, AI engineers, business analysts, and anyone looking to integrate AI and data science into their workflow.

Skills to Learn:

Foundations of AI and machine learning

Advanced data analysis and visualization techniques

AI-driven decision-making and data storytelling

Duration:

This 8-week program includes hands-on projects, workshops, and practical labs to ensure comprehensive learning and application of AI in data science.

4. Google Professional Machine Learning Engineer

Introduction:

Google’s Professional Machine Learning Engineer certification is designed to validate a professional’s ability to design, build, and produce ML models on Google Cloud. It emphasizes hands-on experience with TensorFlow and other Google AI tools.

Who Will Benefit:

Machine learning engineers, data scientists, and developers working with Google Cloud’s AI services.

Skills to Learn:

Designing and building ML models using Google Cloud

Implementing ML pipelines with TensorFlow

Analyzing and optimizing ML models for performance

Duration:

The certification requires around 40 hours of study and hands-on practice, followed by an exam.

More info on Google Machine Learning Engineer

5. IBM AI Engineering Professional Certificate

Introduction:

IBM’s AI Engineering Professional Certificate provides a deep dive into AI and machine learning, including tools like TensorFlow, PyTorch, and IBM Watson. It’s perfect for professionals looking to implement AI solutions across various industries.

Who Will Benefit:

AI engineers, data scientists, and developers interested in using IBM’s AI tools and frameworks.

Skills to Learn:

Building and deploying AI models using TensorFlow and PyTorch

Understanding deep learning and neural networks

Implementing AI solutions with IBM Watson

Duration:

The certification takes approximately 3–6 months to complete, depending on your pace and prior experience with AI tools.

More info on IBM AI Engineering Certificate

6. Cisco Certified DevNet Professional

Introduction:

Cisco’s Certified DevNet Professional certification includes AI and machine learning concepts within the context of network automation. It’s ideal for network professionals who want to incorporate AI into their automation workflows.

Who Will Benefit:

Network engineers, software developers, and automation professionals interested in integrating AI and machine learning into network solutions.

Skills to Learn:

Implementing AI-driven network automation

Using machine learning for network security and performance analysis

Developing and deploying AI applications for networks

Duration:

The certification requires around 60 hours of study and hands-on labs, followed by exams.

More info on Cisco DevNet Professional

7. Certified Artificial Intelligence Practitioner (CAIP) — CertNexus

Introduction:

Certified Artificial Intelligence Practitioner (CAIP) by CertNexus provides a comprehensive overview of AI concepts and practices, from machine learning to deep learning. It focuses on applying AI techniques across various domains.

Who Will Benefit:

AI practitioners, data scientists, and IT professionals looking to implement AI solutions in diverse industries.

Skills to Learn:

Fundamentals of AI and machine learning

Implementing AI models and algorithms

Ethical considerations and AI governance

Duration:

The certification involves approximately 40 hours of study and hands-on practice, followed by an exam.

More info on CertNexus CAIP

8. Adobe Sensei Certification

Introduction:

Adobe Sensei Certification focuses on AI and machine learning within Adobe’s suite of products. It’s designed for creative professionals who want to leverage AI for automating tasks like image recognition, video editing, and content creation.

Who Will Benefit:

Graphic designers, video editors, and digital marketers looking to use AI for creative automation and optimization.

Skills to Learn:

Utilizing Adobe Sensei for image and video editing automation

AI-driven content creation and optimization

Implementing AI for marketing insights and audience targeting

Duration:

This certification typically requires around 20–30 hours of study, followed by practical assessments.

More info on Adobe Sensei

9. Artificial Intelligence Certification — Stanford University

Introduction:

Stanford University’s Artificial Intelligence Certification offers an in-depth exploration of AI, including natural language processing, robotics, and machine learning. It’s one of the most prestigious programs for professionals seeking advanced knowledge in AI.

Who Will Benefit:

AI researchers, engineers, and developers aiming for advanced roles in AI development and research.

Skills to Learn:

Advanced machine learning and neural networks

Robotics and AI in automation

Natural language processing and AI ethics

Duration:

The program duration varies, typically taking around 9–12 months to complete, with both online and in-person options.

More info on Stanford AI Certification

10. Deep Learning Specialization — Coursera

Introduction:

The Deep Learning Specialization on Coursera, offered by Andrew Ng, covers deep learning fundamentals, including neural networks, convolutional networks, and sequence models. It’s perfect for professionals looking to specialize in deep learning techniques.

Who Will Benefit:

AI and machine learning engineers, data scientists, and researchers focusing on deep learning applications.

Skills to Learn:

Building and training neural networks

Implementing convolutional and recurrent neural networks

Applying deep learning to computer vision and NLP

Duration:

This specialization takes approximately 3–4 months to complete, with flexible learning options.

More info on Deep Learning Specialization

The Growing Demand for AI Certifications

According to a 2023 report by Gartner, the demand for AI and machine learning skills has increased by 74% over the past four years. AI certifications not only validate your skills but also enhance your credibility and marketability in a rapidly evolving job market. Companies are seeking professionals who can effectively implement AI solutions to drive innovation and efficiency.

These top 10 AI certifications provide a pathway to mastering AI technologies, ensuring you stay ahead in this competitive field. Whether you’re a data scientist, developer, or AI enthusiast, these certifications offer the knowledge and skills needed to excel in various industries, from technology and healthcare to finance and marketing.

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manageditserviceslosangeles · 6 months

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Maximize Success: Transform Your Life with Azure OpenAI

Artificial Intelligence (AI) has evolved significantly. It transitioned from fiction to an integral part of our daily lives and business operations. In business, AI has shifted from a luxury to an essential tool. It helps analyze data, automate tasks, improve customer experiences, and strategize decisions.

McKinsey’s report suggests AI could contribute $13 trillion to the global economy by 2030. Amidst the ever-changing tech landscape, Azure Open AI stands out as an unstoppable force.

In this blog, we’ll delve into the life-changing impact of Azure Open AI features. We’ll explore how its integration can better workflows, enhance decision-making, and drive unparalleled innovation. Join us on a journey to uncover Azure Open AI to reshape business operations in the modern era.

#azure ai #azure open ai desktop #azure open ai pricing #Azure open ai services #azure ai and ml services

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ahansaxena12 · 4 days

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Best Courses for Software Development at MindStick: Empowering the Next Generation of Innovators

At MindStick, our mission is simple: We train people for the upcoming future technology with the help of our courses and training programs. In a rapidly evolving digital landscape, staying updated with the latest trends and technologies is crucial for success in the field of software development. Whether you're a novice or a seasoned professional, our carefully curated courses will help you stay competitive and prepared for tomorrow's technological challenges.

1. Introduction to Programming with Python

Python has cemented its place as one of the most versatile and widely used programming languages in the tech industry. Whether you’re a beginner or looking to expand your coding skills, our Introduction to Programming with Python course offers a solid foundation.

Why Python?

Beginner-friendly and widely applicable.

Used in web development, data science, automation, and AI.

Simple syntax that emphasizes readability and efficiency.

By the end of the course, you’ll be able to develop basic Python applications and understand fundamental programming concepts like loops, conditions, and functions. With Python's growing demand, mastering it can open doors to countless opportunities across industries.

2. Full Stack Web Development

If you aspire to become a versatile web developer, mastering both front-end and back-end technologies is essential. Our Full Stack Web Development course equips you with the skills needed to build and deploy complete web applications from scratch.

Course Highlights:

Front-end development with HTML5, CSS3, and JavaScript frameworks like React or Angular.

Back-end development using Node.js, Django, or Flask.

Database management with SQL and NoSQL.

Deployment and version control using Git and GitHub.

This course not only covers the technical aspects of web development but also focuses on building scalable, responsive, and user-friendly websites. By becoming proficient in both front-end and back-end development, you’ll be able to handle end-to-end software development processes.

3. Mobile App Development (iOS and Android)

In today's mobile-first world, mobile app development is a critical skill for developers. Our Mobile App Development course covers both iOS and Android platforms, enabling you to create native and cross-platform applications.

Why This Course?

Learn native development with Swift (iOS) and Kotlin (Android).

Explore cross-platform development with Flutter or React Native.

Design intuitive user interfaces with mobile-specific tools.

Deploy applications to the App Store and Google Play.

Mobile apps are central to modern digital experiences. Through this course, you’ll learn how to develop efficient, interactive, and high-performing apps, positioning you as an essential player in the ever-growing mobile market.

4. Cloud Computing and DevOps

As businesses continue migrating to the cloud, understanding cloud architecture and DevOps practices has become a highly sought-after skill set. Our Cloud Computing and DevOps course introduces you to the world of cloud infrastructure and deployment strategies.

Course Focus:

Master the fundamentals of cloud platforms like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure.

Learn Continuous Integration/Continuous Deployment (CI/CD) processes for seamless development.

Explore containerization with Docker and orchestration with Kubernetes.

Gain hands-on experience with automation tools to streamline software development.

By the end of this course, you’ll know how to deploy, manage, and scale applications on cloud platforms, preparing you for future challenges in cloud-based software development.

5. Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of technological advancement, reshaping industries like healthcare, finance, and automotive. Our Artificial Intelligence and Machine Learning course is designed to equip you with the skills to work with these cutting-edge technologies.

What You’ll Learn:

Foundational AI and ML concepts, including Supervised, Unsupervised, and Reinforcement Learning.

Practical applications using TensorFlow and PyTorch.

Data preprocessing, feature engineering, and model evaluation.

Hands-on projects in areas like Natural Language Processing (NLP) and Computer Vision.

This course will empower you to build intelligent systems capable of analyzing vast datasets and making predictions, a skill set that is highly in demand across all industries.

6. Cybersecurity and Ethical Hacking

With the increasing frequency of cyber-attacks, there’s an urgent need for professionals trained in cybersecurity and ethical hacking. Our comprehensive course in Cybersecurity and Ethical Hacking offers hands-on learning for identifying vulnerabilities and securing networks.

Course Focus:

Learn the essentials of network security and encryption.

Conduct penetration testing and vulnerability assessments.

Explore ethical hacking techniques to safeguard systems.

Understand security best practices for developing secure software.

In a world where data breaches are becoming increasingly common, cybersecurity skills are in high demand. Completing this course will allow you to protect organizations from emerging cyber threats and make you a valuable asset in the IT security landscape.

Conclusion

At MindStick, we train people for the upcoming future technology by offering courses that address the most critical and in-demand skills in software development. Whether you're aiming to become a full-stack developer, dive into AI and machine learning, or specialize in cybersecurity, our programs are designed to prepare you for success in the fast-evolving tech world.

By investing in your education with MindStick, you’re not just learning the tools of today—you’re preparing for the innovations of tomorrow.

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mvishnukumar · 5 days

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What are the leading companies in the USA offering machine learning services, and what sets them apart?

Hi,

Several leading companies in the USA offer machine learning services, each with its unique strengths:

Google Cloud AI:

Google offers a wide range of AI and machine learning tools, including TensorFlow, Vertex AI, and AutoML. What sets Google apart is its massive infrastructure, advanced machine learning research, and user-friendly cloud solutions.

Amazon Web Services (AWS):

AWS provides machine learning services like SageMaker, which allows developers to build, train, and deploy models quickly. AWS’s strength lies in its scalability and variety of integrated services for different AI and ML needs.

Microsoft Azure AI:

Microsoft offers Azure AI and Machine Learning, with tools like Azure Cognitive Services and Azure Machine Learning Studio. Their strong integration with existing enterprise tools and services makes it ideal for large organizations.

IBM Watson:

IBM offers Watson, a powerful AI platform designed for large-scale machine learning and deep learning tasks. Its strength lies in advanced natural language processing (NLP) capabilities.

Each of these companies provides powerful tools, but the choice depends on the specific needs of a business, such as scalability, ease of use, or specialized AI functions.

#data analytics #python #data science course #lejhro #big data #data privacy #machinelearning #datascience #ai #bigdata

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carrergrowth · 8 days

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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.

#tech jobs in the US

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argoslabs · 12 days

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Getting Started with AI: A Guide for Businesses!

As businesses across industries continue to embrace digital transformation, integrating Artificial Intelligence (AI) has become a key driver of growth and innovation. AI technologies can enhance decision-making, improve operational efficiency, and provide personalized customer experiences. If you’re ready to take the leap into the world of AI, this guide will outline the essential steps to get started and successfully implement AI in your business.

Understanding the Basics of AI:

Before diving into AI integration, it’s crucial to understand the fundamentals of AI and its various applications. AI refers to the simulation of human intelligence in machines programmed to think and learn like humans. Key components of AI include:

Machine Learning (ML): A subset of AI that enables systems to learn from data and improve over time without explicit programming.

Natural Language Processing (NLP): Allows machines to understand and respond to human language, facilitating interactions between humans and machines.

Computer Vision: Enables machines to interpret and make decisions based on visual data, such as images and videos.

Robotics: Combines AI with physical machines to automate tasks and enhance productivity.

Understanding these components will help you identify the right AI solutions for your business needs.

Step 1: Identify Business Goals and Use Cases:

The first step in integrating AI into your business is to identify your goals and potential use cases. Consider the challenges you face and how AI can help address them. Common applications of AI in business include:

Customer Service: Streamlining Frequently Asked Questions or Very Key Guidelines for instant information access and support through automation.

Data Analysis: Utilizing AI to analyze large datasets for insights that inform decision-making.

Sales and Marketing: Leveraging AI for personalized marketing campaigns and lead scoring.

Supply Chain Management: Enhancing inventory management and demand forecasting through predictive analytics.

Clearly defining your goals will guide the selection of AI technologies and solutions that align with your business objectives.

Step 2: Assess Data Readiness:

Data is the foundation of AI. Before implementing AI solutions, assess your organization’s data readiness. Consider the following:

Data Quality: Ensure that your data is accurate, complete, and relevant. Poor-quality data can lead to inaccurate AI models and unreliable insights.

Data Availability: Identify the data sources you have access to, including internal systems, databases, and external data providers.

Data Privacy: Comply with data protection regulations, such as GDPR or CCPA, to ensure the ethical use of customer data.

If your organization lacks sufficient data, consider investing in data collection and management processes to build a solid foundation for AI implementation.

Step 3: Choose the Right AI Tools and Technologies:

With your goals defined and data assessed, it’s time to select the right AI tools and technologies for your business. Consider the following options:

Pre-built AI Solutions: Many companies offer off-the-shelf AI solutions that can be easily integrated into existing systems. Examples include AI chatbots, CRM systems with AI capabilities, and analytics platforms.

Custom AI Development: For more complex use cases, consider developing custom AI solutions tailored to your specific needs. This may involve collaborating with AI developers or data scientists.

Cloud-based AI Services: Many cloud providers offer AI services that allow businesses to access powerful AI tools without significant upfront investment. Services like Google AI, AWS AI, and Microsoft Azure provide machine learning capabilities, NLP, and more.

Evaluate your budget, technical expertise, and business needs when selecting AI tools and technologies.

Step 4: Build a Cross-Functional Team:

Successful AI integration requires collaboration across various departments. Form a cross-functional team that includes members from IT, data science, marketing, sales, and operations. This diverse team will bring different perspectives and expertise to the project, ensuring a comprehensive approach to AI implementation.

Consider hiring or training data scientists and AI specialists who can lead the development and deployment of AI solutions. Continuous learning and upskilling of existing employees in AI-related areas will also contribute to successful integration.

Step 5: Start with Pilot Projects:

Before fully rolling out AI solutions, consider starting with pilot projects. Pilot projects allow you to test AI applications on a smaller scale, evaluate their effectiveness, and gather valuable insights for broader implementation.

Define Success Metrics: Establish clear success metrics to measure the impact of the pilot project. This could include metrics like improved customer satisfaction, increased sales, or reduced operational costs.

Gather Feedback: Collect feedback from stakeholders involved in the pilot project to identify challenges and areas for improvement.

Iterate and Optimize: Use the insights gained from the pilot project to iterate and optimize your AI solutions before a full-scale rollout.

Step 6: Monitor and Evaluate Performance:

Once you’ve implemented AI solutions across your organization, continuous monitoring and evaluation are crucial to ensuring their effectiveness. Track key performance indicators (KPIs) related to your AI initiatives, such as:

Customer Engagement: Measure the impact of AI on customer interactions and satisfaction.

Operational Efficiency: Analyze improvements in productivity and cost savings.

Revenue Growth: Evaluate the effect of AI on sales and revenue generation.

Regularly review performance data and make adjustments as needed to optimize your AI initiatives and achieve your business goals.

Step 7: Foster a Culture of Innovation:

Integrating AI into your business is not just a one-time project; it’s an ongoing journey. To fully leverage the potential of AI, foster a culture of innovation within your organization. Encourage employees to explore new ideas, experiment with AI technologies, and stay informed about emerging trends in AI.

Provide opportunities for continuous learning and professional development in AI-related fields. By creating an environment that values innovation and embraces change, your organization will be better positioned to adapt to future advancements in AI technology.

Conclusion

Integrating AI into your business is a transformative journey that can unlock new opportunities for growth and innovation. By following these essential steps—defining your goals, assessing data readiness, selecting the right tools, building a cross-functional team, starting with pilot projects, monitoring performance, and fostering a culture of innovation—you can successfully navigate the path to AI integration.

At Argos Labs, we are dedicated to helping businesses harness the power of AI for growth and success. Our expertise in AI solutions empowers organizations to unlock valuable insights, enhance customer experiences, and drive operational efficiency. Ready to take the next step in your AI journey? Let’s get started!

Reference:

Unveiling the Future: Hyperautomation Trends Transforming Industries

The Power of Automation with Argos Labs: Your Python-Based Low-Code Solution

Embracing Digital Transformation: How Argos Labs is Revolutionizing Business with Process Automation and AI

Streamline Your Business Processes with Argos Labs Low-code Python For more visit ,

https://www.linkedin.com/company/argoslabs/posts/?feedView=articles

#automation #lowcode #guide

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harperlewisblogging · 16 days

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6 Key Trends in Cloud Computing for 2024

Cloud computing continues to reshape the IT landscape, driving innovations and efficiencies across industries. As we advance into 2024, several emerging trends are poised to influence how businesses and organizations leverage cloud technologies. In this comprehensive analysis, we delve into the six key trends in cloud computing for 2024, exploring how these developments will impact the industry and transform business operations.

1. Increased Adoption of Multi-Cloud Strategies

In 2024, the multi-cloud strategy is expected to become more prevalent. Organizations are increasingly adopting multiple cloud providers to avoid vendor lock-in and leverage the unique strengths of each platform. This approach allows businesses to optimize their cloud environments by choosing best-of-breed services from various providers.

The benefits of multi-cloud strategies include enhanced resilience and flexibility. Companies can distribute workloads across multiple clouds, minimizing the risk of downtime and improving disaster recovery capabilities. Additionally, multi-cloud environments can offer better compliance and data sovereignty, as businesses can select regions that align with regulatory requirements.

To manage multi-cloud environments effectively, businesses are turning to cloud management platforms and orchestration tools. These solutions help in monitoring, automating, and optimizing resources across different cloud environments, ensuring seamless integration and operational efficiency.

2. Accelerated Growth of Edge Computing

Edge computing is set to gain significant traction in 2024. This paradigm shift involves processing data closer to the source of generation rather than relying solely on centralized cloud data centers. Edge computing enables real-time data processing, which is crucial for applications requiring low latency and high performance.

The rise of Internet of Things (IoT) devices, autonomous vehicles, and smart cities is driving the need for edge computing. By processing data at the edge of the network, businesses can achieve faster insights, reduce bandwidth usage, and enhance security. This trend is also contributing to the growth of 5G technology, which provides the high-speed connectivity necessary for effective edge computing.

3. Enhanced Focus on Cloud Security and Privacy

As cloud adoption increases, so does the emphasis on cloud security and privacy. In 2024, safeguarding sensitive data and ensuring robust security measures will be a top priority for organizations. Cloud providers are continuously enhancing their security frameworks, incorporating advanced technologies such as artificial intelligence (AI) and machine learning (ML) to detect and mitigate threats.

Businesses are also adopting Zero Trust Architecture to bolster security. This model requires strict verification for every user and device attempting to access cloud resources, regardless of their location. Additionally, organizations are implementing end-to-end encryption and regular security audits to protect data and ensure compliance with industry regulations.

4. Rise of Serverless Computing

Serverless computing is revolutionizing how developers build and deploy applications. In 2024, the popularity of serverless architectures is expected to soar, as they offer significant advantages in terms of scalability, cost-efficiency, and reduced operational overhead.

Serverless computing allows developers to focus on writing code without worrying about managing servers or infrastructure. This model automatically scales resources based on demand and charges only for the actual usage, leading to optimized cost management. Major cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), are expanding their serverless offerings, making it easier for businesses to adopt this technology.

5. Advancements in Cloud-Native Technologies

Cloud-native technologies are becoming increasingly integral to modern software development. In 2024, we will see continued advancements in containerization, microservices, and DevOps practices, all of which contribute to the agile and scalable nature of cloud-native applications.

Software development services are also being transformed by these innovations. As organizations increasingly adopt cloud-native technologies and serverless computing, they are reshaping their software development strategies to leverage these advancements. Software development services now incorporate containerization and microservices architectures to build more agile and scalable applications. These technologies enable faster deployment, enhanced flexibility, and streamlined operations. Additionally, the integration of AI and ML within cloud environments is revolutionizing how software solutions are developed, enabling more sophisticated data analysis, automation, and predictive capabilities. By aligning with these trends, software development services can offer innovative solutions that meet the growing demands of modern businesses.

Containers, such as those managed by Kubernetes, enable developers to package applications and their dependencies into isolated environments, ensuring consistency across different stages of development and deployment. Microservices architectures break down applications into smaller, manageable services, enhancing flexibility and enabling faster updates. Meanwhile, DevOps practices foster collaboration between development and operations teams, accelerating the delivery of new features and improvements.

6. Emergence of Artificial Intelligence and Machine Learning in the Cloud

The integration of artificial intelligence (AI) and machine learning (ML) with cloud computing is a trend set to dominate in 2024. Cloud providers are increasingly offering AI and ML services as part of their portfolios, enabling businesses to harness the power of these technologies without requiring extensive in-house expertise.

AI and ML services in the cloud facilitate a range of applications, from predictive analytics and personalization to automated decision-making and natural language processing. Organizations are leveraging these tools to gain deeper insights from their data, optimize operations, and enhance customer experiences. Cloud-based AI and ML platforms also provide scalability and flexibility, allowing businesses to experiment with and deploy advanced models efficiently.

Conclusion

As we move further into 2024, the landscape of cloud computing will continue to evolve rapidly. The adoption of multi-cloud strategies, the growth of edge computing, enhanced focus on cloud security, the rise of serverless computing, advancements in cloud-native technologies, and the integration of AI and ML are set to define the next wave of cloud innovations.

By staying abreast of these trends, organizations can leverage cloud technologies to drive operational excellence, foster innovation, and achieve a competitive edge. Embracing these developments will be crucial for businesses looking to navigate the dynamic cloud computing environment of 2024 and beyond.

#cloud computing

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