#azure ai and ml services

[manageditserviceslosangeles]

Everything you wanted to know about Azure OpenAI Pricing

Azure OpenAI is a powerful tool for businesses, integrating seamlessly with Azure for advanced AI capabilities. It automates processes like natural language processing and image recognition, enhancing efficiency. By leveraging these advances, businesses can automate tasks, improve operations, and unleash limitless potential.

In this blog, we provide detailed insights into Azure OpenAI pricing, helping you understand the costs associated with each service. Our expert guidance ensures you can navigate these options effectively, optimizing your AI investment.

Feel free to reach out if you have questions or need clarification on using Azure OpenAI services, including their use cases. Contact us for detailed pricing information and personalized support.

📞 Phone: +1(702) 780-7900

 ✉️ Email: info@ecfdata.com

[cybtekk]

Artificial Intellligence & Machine Learning Solutions | AI/ML Services

As we step into a new era of AI/ML, businesses can unlock unprecedented advantages, leveraging the power of data-driven insights, automated processes, intelligent decision-making, and transformative innovation to gain a competitive edge and drive exponential growth.

[talentfolder]

The Future of Jobs in IT: Which Skills You Should Learn.

With changes in the industries due to technological changes, the demand for IT professionals will be in a constant evolution mode. New technologies such as automation, artificial intelligence, and cloud computing are increasingly being integrated into core business operations, which will soon make jobs in IT not just about coding but about mastering new technologies and developing versatile skills. Here, we cover what is waiting to take over the IT landscape and how you can prepare for this future.

1. Artificial Intelligence (AI) and Machine Learning (ML):

AI and ML are the things that are currently revolutionizing industries by making machines learn from data, automate processes, and predict outcomes. Thus, jobs for the future will be very much centered around these fields of AI and ML, and the professionals can expect to get work as AI engineers, data scientists, and automation specialists.

2. Cloud Computing:

With all operations now moving online, architects, developers, and security experts are in high demand for cloud work. It is very important to have skills on platforms such as AWS, Microsoft Azure, and Google Cloud for those who wish to work on cloud infrastructure and services.

3. Cybersecurity:

As dependence on digital mediums continues to increase, so must cybersecurity measures. Cybersecurity, ethical hacking, and network security would be skills everyone must use to protect data and systems from all the continuous threats.

4. Data Science and Analytics:

As they say, the new oil in this era is data. Therefore, organisations require professionals who would be able to analyze humongous datasets and infer actionable insights. Data science, data engineering, as well as advanced analytics tools, will be your cornucopia for thriving industries in the near future.

5. DevOps and Automation:

DevOps engineers are the ones who ensure that continuous integration and deployment work as smoothly and automatically as possible. Your knowledge of the business/operations will orient you well on that terrain, depending on how that applies to your needs.

Conclusion

IT job prospects rely heavily on AI, cloud computing, cybersecurity, and automation. It means that IT professionals must constantly innovate and update their skills to stay in competition. Whether an expert with years of experience or a newcomer, focusing on the following in-demand skills will gather success in this diverse land of IT evolution.

You might also like: How to crack interview in MNC IT

[mvishnukumar]

How can you optimize the performance of machine learning models in the cloud?

Optimizing machine learning models in the cloud involves several strategies to enhance performance and efficiency. Here’s a detailed approach:

Choose the Right Cloud Services:

Managed ML Services: 

Use managed services like AWS SageMaker, Google AI Platform, or Azure Machine Learning, which offer built-in tools for training, tuning, and deploying models.

Auto-scaling: 

Enable auto-scaling features to adjust resources based on demand, which helps manage costs and performance.

Optimize Data Handling:

Data Storage: 

Use scalable cloud storage solutions like Amazon S3, Google Cloud Storage, or Azure Blob Storage for storing large datasets efficiently.

Data Pipeline: 

Implement efficient data pipelines with tools like Apache Kafka or AWS Glue to manage and process large volumes of data.

Select Appropriate Computational Resources:

Instance Types: 

Choose the right instance types based on your model’s requirements. For example, use GPU or TPU instances for deep learning tasks to accelerate training.

Spot Instances: 

Utilize spot instances or preemptible VMs to reduce costs for non-time-sensitive tasks.

Optimize Model Training:

Hyperparameter Tuning: 

Use cloud-based hyperparameter tuning services to automate the search for optimal model parameters. Services like Google Cloud AI Platform’s HyperTune or AWS SageMaker’s Automatic Model Tuning can help.

Distributed Training: 

Distribute model training across multiple instances or nodes to speed up the process. Frameworks like TensorFlow and PyTorch support distributed training and can take advantage of cloud resources.

Monitoring and Logging:

Monitoring Tools: 

Implement monitoring tools to track performance metrics and resource usage. AWS CloudWatch, Google Cloud Monitoring, and Azure Monitor offer real-time insights.

Logging: 

Maintain detailed logs for debugging and performance analysis, using tools like AWS CloudTrail or Google Cloud Logging.

Model Deployment:

Serverless Deployment: 

Use serverless options to simplify scaling and reduce infrastructure management. Services like AWS Lambda or Google Cloud Functions can handle inference tasks without managing servers.

Model Optimization: 

Optimize models by compressing them or using model distillation techniques to reduce inference time and improve latency.

Cost Management:

Cost Analysis: 

Regularly analyze and optimize cloud costs to avoid overspending. Tools like AWS Cost Explorer, Google Cloud’s Cost Management, and Azure Cost Management can help monitor and manage expenses.

By carefully selecting cloud services, optimizing data handling and training processes, and monitoring performance, you can efficiently manage and improve machine learning models in the cloud.

[azurecertifications1]

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.

[gradsireninc]

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.

[gradsiren]

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 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.

[greenoperator]

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.

[cloudatlasinc]

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.

[manageditserviceslosangeles]

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. 

[iemedugroup]

Top Specializations in Computer Science & Engineering to Consider

Choosing a specialization in computer science and engineering can be a game-changer for your career. With fields like AI and machine learning to cybersecurity and data science, every path offers unique opportunities and challenges. If you're an Indian student considering a tech career, this guide will help you explore top specializations and highlight career opportunities in computer science available in each field.

1. Artificial Intelligence and Machine Learning (AI/ML)

AI and ML aren’t just buzzwords—they’re transforming industries worldwide. This specialization focuses on creating systems that can analyze data and “learn” to make decisions with minimal human intervention. Imagine a healthcare system that detects early signs of disease based on patient data. According to the World Economic Forum, jobs in AI and ML are expected to grow by 37% annually over the next few years, making it one of the most promising areas in tech.

In AI and ML, you can work in various fields, from healthcare to finance, or even entertainment, creating intelligent applications. Indian tech companies and multinational corporations are on the lookout for AI/ML engineers to drive innovation, making this a field with high demand and excellent career prospects.

2. Data Science and Big Data Analytics

With the digital world generating data in massive amounts, there’s a need for professionals who can analyze it. Data science involves turning raw data into insights that guide business decisions. For example, an e-commerce company might use data science to predict shopping trends during the festive season in India, helping them stock up on popular products. According to a report by NASSCOM, data science roles in India have seen a 45% growth over the past year.

Students with strong math skills and analytical thinking will thrive in this field. Career opportunities in computer science for data scientists continue to expand, especially in tech and e-commerce, where understanding user behavior is crucial.

3. Cybersecurity

As the world becomes more digital, protecting information from cyber threats is essential. Cybersecurity experts work to defend systems and data from attacks, using methods like encryption and threat analysis. Think about digital banking: users trust banks with their money and data, and it’s the cybersecurity team that makes sure this trust is upheld.

In India, where digital services are expanding rapidly, cybersecurity has become a major focus. For students interested in tackling real-world challenges and ethical problem-solving, this field offers engaging work and competitive salaries. The demand for cybersecurity professionals is rising, with roles in banking, government, and tech companies.

4. Cloud Computing

Cloud computing is changing the way we store and manage data. Instead of relying on physical servers, companies use cloud platforms like AWS, Microsoft Azure, and Google Cloud to store data and run applications remotely. This is especially important for startups and companies looking to scale without investing in costly infrastructure.

If you're interested in building scalable, flexible data solutions, cloud computing could be an ideal field. This specialization involves working on cloud architecture, managing resources, and ensuring data security. With India’s growing reliance on digital solutions, expertise in cloud computing opens up diverse roles in sectors like tech consulting, finance, and enterprise IT.

5. Robotics and Automation

Ever dreamed of building machines that perform tasks automatically? Robotics and automation bring together mechanical engineering and computer science to create innovative applications, from self-driving cars to factory robots. For instance, the automotive industry in India increasingly relies on robotic automation to improve efficiency in manufacturing.

In robotics, students who enjoy working with both hardware and software will find a unique blend of opportunities. This specialization has high growth potential, especially as industries worldwide continue to explore automation to optimize production.

6. Blockchain Technology

Blockchain is more than just cryptocurrency. At its core, it’s a secure, decentralized way of recording transactions, which is a big advantage in fields that rely on transparency, like finance, supply chain, and healthcare. For example, a supply chain company might use blockchain to track a product’s journey from factory to consumer, ensuring authenticity and accountability at every step.

In India, blockchain is gaining traction, especially in banking and government sectors, where transparency and security are paramount. With more companies adopting blockchain, there’s a growing demand for professionals who understand the technology behind it. If you're interested in finance and tech, this specialization can set you up for a promising career.

7. Human-Computer Interaction (HCI)

HCI is all about designing user-friendly software and systems that create positive experiences for users. Think of an app you love using—it’s probably well-designed, easy to navigate, and intuitive. HCI specialists work on creating such experiences by understanding user needs and behaviors.

As Indian companies increasingly focus on user experience (UX), HCI is becoming an attractive specialization. This field is ideal for those who want to combine design and technology, leading to roles where you help shape the way people interact with digital tools.

8. Embedded Systems

Embedded systems are the "brains" within everyday devices, from washing machines to medical equipment. This specialization is all about creating the systems that enable these devices to function independently. For example, an embedded system in a car can monitor fuel efficiency, detect maintenance needs, and provide valuable feedback to the driver.

For students interested in both hardware and software, embedded systems offer a path into industries focused on automation and the Internet of Things (IoT). As India moves towards more “smart” devices, expertise in embedded systems is increasingly valuable.

Conclusion

Choosing the right specialization in Computer Science & Engineering can set you on a path to an exciting career with ample growth opportunities. Each field, from AI and machine learning to cybersecurity, offers unique roles and career advancement opportunities for students who invest time and effort in mastering their chosen field.

As an Indian student exploring these options, it’s helpful to speak with professionals, seek internships, or take online courses to gain firsthand experience in your area of interest. Start by exploring entry-level certifications or hands-on projects, and connect with mentors who can guide you. The right specialization can open doors and help you make a meaningful impact in the ever-evolving tech world.

[govindhtech]

IBM Db2 AI Updates: Smarter, Faster, Better Database Tools

IBM Db2

Designed to handle mission-critical workloads worldwide.

What is IBM Db2?

IBM Db2 is a cloud-native database designed to support AI applications at scale, real-time analytics, and low-latency transactions. It offers database managers, corporate architects, and developers a single engine that is based on decades of innovation in data security, governance, scalability, and availability.

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When moving to hybrid deployments, create the next generation of mission-critical apps that are available 24/7 and have no downtime across all clouds.

Support for all contemporary data formats, workloads, and programming languages will streamline development.

Support for open formats, including Apache Iceberg, allows teams to safely communicate data and information, facilitating quicker decision-making.

Utilize IBM Watsonx integration for generative artificial intelligence (AI) and integrated machine learning (ML) capabilities to implement AI at scale.

Use cases

Power next-gen AI assistants

Provide scalable, safe, and accessible data so that developers may create AI-powered assistants and apps.

Build new cloud-native apps for your business

Create cloud-native applications with low latency transactions, flexible scalability, high concurrency, and security that work on any cloud. Amazon Relational Database Service (RDS) now offers it.

Modernize mission-critical web and mobile apps

Utilize Db2 like-for-like compatibility in the cloud to modernize your vital apps for hybrid cloud deployments. Currently accessible via Amazon RDS.

Power real-time operational analytics and insights

Run in-memory processing, in-database analytics, business intelligence, and dashboards in real-time while continuously ingesting data.

Data sharing

With support for Apache Iceberg open table format, governance, and lineage, you can share and access all AI data from a single point of entry.

In-database machine learning

With SQL, Python, and R, you can create, train, assess, and implement machine learning models from inside the database engine without ever transferring your data.

Built for all your workloads

IBM Db2 Database

Db2 is the database designed to handle transactions of any size or complexity. Currently accessible via Amazon RDS.

IBM Db2 Warehouse

You can safely and economically conduct mission-critical analytical workloads on all kinds of data with IBM Db2 Warehouse. Watsonx.data integration allows you to grow AI workloads anywhere.

IBM Db2 Big SQL

IBM Db2 Big SQL is a high-performance, massively parallel SQL engine with sophisticated multimodal and multicloud features that lets you query data across Hadoop and cloud data lakes.

Deployment options

You require an on-premises, hybrid, or cloud database. Use Db2 to create a centralized business data platform that operates anywhere.

Cloud-managed service

Install Db2 on Amazon Web Services (AWS) and IBM Cloud as a fully managed service with SLA support, including RDS. Benefit from the cloud’s consumption-based charging, on-demand scalability, and ongoing improvements.

Cloud-managed container

Launch Db2 as a cloud container:integrated Db2 into your cloud solution and managed Red Hat OpenShift or Kubernetes services on AWS and Microsoft Azure.

Self-managed infrastructure or IaaS

Take control of your Db2 deployment by installing it as a conventional configuration on top of cloud-based infrastructure-as-a-service or on-premises infrastructure.

IBM Db2 Updates With AI-Powered Database Helper

Enterprise data is developing at an astonishing rate, and companies are having to deal with ever-more complicated data environments. Their database systems are under more strain than ever as a result of this. Version 12.1 of IBM’s renowned Db2 database, which is scheduled for general availability this week, attempts to address these demands. The latest version redefines database administration by embracing AI capabilities and building on Db2’s lengthy heritage.

The difficulties encountered by database administrators who must maintain performance, security, and uptime while managing massive (and quickly expanding) data quantities are covered in Db2 12.1. A crucial component of their strategy is IBM Watsonx’s generative AI-driven Database Assistant, which offers real-time monitoring, intelligent troubleshooting, and immediate replies.

Introducing The AI-Powered Database Assistant

By fixing problems instantly and averting interruptions, the new Database Assistant is intended to minimize downtime. Even for complicated queries, DBAs may communicate with the system in normal language to get prompt responses without consulting manuals.

The Database Assistant serves as a virtual coach in addition to its troubleshooting skills, speeding up DBA onboarding by offering solutions customized for each Db2 instance. This lowers training expenses and time. By enabling DBAs to address problems promptly and proactively, the database assistant should free them up to concentrate on strategic initiatives that improve the productivity and competitiveness of the company.

IBM Db2 Community Edition

Now available

Db2 12.1

No costs. No adware or credit card. Simply download a single, fully functional Db2 Community License, which you are free to use for as long as you wish.

What you can do when you download Db2

Install on a desktop or laptop and use almost anywhere. Join an active user community to discover events, code samples, and education, and test prototypes in a real-world setting by deploying them in a data center.

Limits of the Community License

Community license restrictions include an 8 GB memory limit and a 4 core constraint.

Read more on govindhtech.com

[flentas]

Cloud Modernization: Essential Trends Shaping the Future of Cloud Migration

Cloud modernization has emerged as a cornerstone for businesses aiming to stay competitive and resilient in today’s digital world. Organizations in finance, retail, healthcare, and beyond are increasingly moving toward cloud modernization to streamline operations, optimize costs, and harness the power of next-generation technology. Here’s an in-depth look at the latest cloud modernization trends that businesses are embracing to elevate their cloud migration journey. 

Emphasis on Multi-Cloud and Hybrid Cloud Strategies

Businesses are increasingly adopting multi-cloud and hybrid cloud environments to avoid vendor lock-in, optimize resources, and ensure better control over data management. This trend enables companies to: 

Enhance Flexibility: Leverage the best services from multiple providers like AWS, Azure, and Google Cloud. 

Improve Disaster Recovery: Spread workloads across different environments to mitigate risks and ensure business continuity. 

Optimize Cost Efficiency: Choose the most cost-effective cloud resources for each workload. 

Hybrid cloud, which combines on-premises infrastructure with public and private cloud, is particularly attractive for industries with stringent data compliance requirements, such as banking and healthcare. 

Incorporation of Artificial Intelligence (AI) and Machine Learning (ML) in Cloud Environments

Integrating AI and ML into cloud environments enables businesses to make data-driven decisions and automate complex processes. Leading cloud providers now offer AI and ML tools that simplify the deployment of these technologies at scale. 

Enhanced Customer Insights: AI-powered analytics can identify patterns, behaviors, and trends in real-time, improving customer experience and personalization. 

Operational Automation: From predictive maintenance to inventory management, AI optimizes business operations by reducing manual efforts. 

Intelligent Security: ML models enhance security by detecting unusual behavior and preventing potential breaches. 

For cloud migration, AI-powered tools can even assist with assessing and planning migration strategies, speeding up deployment, and minimizing downtime. 

Serverless Computing and Function-as-a-Service (FaaS)

Serverless computing and Function-as-a-Service (FaaS) are enabling businesses to focus more on application development and innovation, leaving server management to cloud providers. Serverless architecture offers: 

Scalability on Demand: Automatically scales resources as required, making it ideal for unpredictable workloads. 

Reduced Operational Costs: No need to maintain servers, leading to significant cost savings. 

Faster Time-to-Market: Developers can focus solely on code, accelerating deployment. 

This trend is particularly relevant to cloud migration as it enables companies to move away from costly and time-consuming server maintenance, allowing them to fully leverage cloud-native applications. 

Rise of Cloud-Native Applications and Microservices Architecture

The development of cloud-native applications based on microservices architecture is gaining traction. This approach breaks down applications into small, loosely coupled services that can be developed, deployed, and scaled independently. 

Agility: Microservices promote continuous integration and delivery (CI/CD), accelerating development cycles. 

Scalability: Components can be scaled independently based on demand. 

Resilience: If one service fails, it won’t affect the entire application. 

For organizations migrating to the cloud, adopting cloud-native design patterns helps modernize legacy applications, making them more adaptable, resilient, and aligned with the cloud’s architecture. 

Security-First Cloud Modernization

With the rise in cybersecurity threats, companies are investing heavily in cloud-native security tools to protect their data and ensure regulatory compliance. Current trends in cloud security focus on: 

Zero-Trust Architecture: A framework that enforces strict identity verification across all access points. 

Encryption Everywhere: Data is encrypted both in transit and at rest, protecting sensitive information from unauthorized access. 

Automated Threat Detection: Using AI and ML, cloud platforms now offer tools that detect and respond to anomalies in real time. 

These security strategies are vital for companies in regulated industries and are critical during cloud migration, ensuring data remains secure throughout the process. 

Cloud Cost Optimization and FinOps

With more organizations scaling their cloud use, cloud cost optimization has become essential. FinOps—a combination of financial management practices and cloud operational management is becoming a standard practice for cloud cost governance. 

Cost Visibility: Provides transparency into spending, allowing teams to optimize resources. 

Resource Allocation: Ensures resources are only used when needed and are appropriately sized. 

Collaboration Between Teams: Aligns financial and technical teams to create a culture of accountability. 

FinOps is crucial during cloud migration, helping organizations maintain a balance between performance and cost, particularly as new workloads are introduced. 

Edge Computing as an Extension of Cloud Services

Edge computing, which processes data closer to its source, is rising as an extension of cloud services. By reducing latency, edge computing offers businesses: 

Real-Time Data Processing: Processes data closer to its source, ideal for IoT devices and time-sensitive applications. 

Reduced Latency: Enables near-instantaneous data processing, crucial for applications requiring quick response times. 

Improved User Experience: Reduces the need to route data back to a centralized cloud, enhancing the user experience. 

This trend is especially useful for industries such as manufacturing and retail, where IoT data is collected and analyzed in real time, ensuring swift and accurate decision-making. 

Low-Code and No-Code Development in Cloud Environments

Low-code and no-code development platforms are revolutionizing how businesses create and deploy applications in the cloud. These platforms empower non-technical users to contribute to application development, leading to: 

Faster Application Deployment: Build applications without extensive coding, reducing time-to-market. 

Empowerment of Business Units: Enables business units to create solutions without relying heavily on IT. 

Cost Savings: Reduces the dependency on large development teams, lowering operational costs. 

For cloud migration, low-code and no-code platforms ease the transition by enabling teams to recreate legacy systems in the cloud quickly and at a lower cost. 

Conclusion 

As cloud modernization continues to evolve, businesses have a unique opportunity to transform how they operate, innovate, and serve their customers. By embracing these trends—whether through hybrid and multi-cloud environments, AI integration, microservices architecture, or robust security practices—organizations can fully unlock the benefits of cloud technology. 

For companies looking to embark on a cloud migration journey, understanding and leveraging these trends is essential for building a resilient, scalable, and future-ready cloud infrastructure.

[atplblog]

Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] Learn using Cloud data technologies for improving data analytics and decision-making capabilities for your organization Description Cloud data architectures are a valuable tool for organizations that want to use data to make better decisions. By understanding the different components of Cloud data architectures and the benefits they offer, organizations can select the right architecture for their needs. This book is a holistic guide for using Cloud data technologies to ingest, transform, and analyze data. It covers the entire data lifecycle, from collecting data to transforming it into actionable insights. The readers will get a comprehensive overview of Cloud data technologies and AI/ML algorithms. The readers will learn how to use these technologies and algorithms to improve decision-making, optimize operations, and identify new opportunities. By the end of the book, you will have a comprehensive understanding of loud data architectures and the confidence to implement effective solutions that drive business success. What you will learn ● Learn the fundamental principles of data architecture. ● Understand the working of different cloud ecosystems such as AWS, Azure & GCP. ● Explore different Snowflake data services. ● Learn how to implement data governance policies and procedures. Who this book is for This book is for executives, IT professionals, and data enthusiasts who want to learn more about Cloud data architectures. It does not require any prior experience, but a basic understanding of data concepts and technology landscapes will be helpful. Table of Contents 1. Data Architectures and Patterns 2. Enterprise Data Architectures 3. Cloud Fundamentals 4. Azure Data Eco-system 5. AWS Data Services 6. Google Data Services 7. Snowflake Data Eco-system 8. Data Governance 9. Data Intelligence: AI-ML Modeling and Services Publisher ‏ : ‎ BPB Publications (26 September 2023) Language ‏ : ‎ English Paperback ‏ : ‎ 240 pages ISBN-10 ‏ : ‎ 9355515812 ISBN-13 ‏ : ‎ 978-9355515810 Reading age ‏ : ‎ 18 years and up Item Weight ‏ : ‎ 480 g Dimensions ‏ : ‎ 24.3 x 1.3 x 19.3 cm Country of Origin ‏ : ‎ India [ad_2]

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An Overview of Saudi Arabia Enterprises ICT Investment Market: Trends and Insights

The ICT investment market in Saudi Arabia has seen substantial growth as the nation pursues its Vision 2030 objectives, a national agenda aimed at economic diversification and digital transformation. With enterprises across sectors embracing advanced technologies, the ICT investment landscape in Saudi Arabia is marked by a robust focus on cloud computing, cybersecurity, AI, and IoT solutions.

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Below is an overview of the key trends, insights, and factors driving ICT investments in the Kingdom.

1. Market Drivers

Vision 2030 Initiative: Vision 2030 is a significant driver for ICT investments in Saudi Arabia, as it aims to diversify the economy beyond oil by fostering innovation, modernizing infrastructure, and enabling a knowledge-based economy.

Increased Digital Transformation: Saudi enterprises are rapidly adopting digital solutions to enhance efficiency, scalability, and productivity. This includes investments in cloud computing, big data analytics, and AI.

Growing E-Commerce Sector: As online retail gains traction in Saudi Arabia, there is heightened investment in ICT infrastructure to support e-commerce platforms, logistics solutions, and secure payment gateways.

Government Support and Funding: The Saudi government is a key player in promoting ICT initiatives, offering incentives, subsidies, and grants to enterprises investing in digital transformation. Programs like the National Transformation Program (NTP) bolster the ICT sector.

2. Key Trends

Cloud Computing and Data Centers: Cloud adoption has surged in Saudi Arabia, especially as enterprises look for cost-effective and scalable storage and computing solutions. Both public and private sector organizations are migrating workloads to the cloud. Leading cloud providers, such as AWS, Google Cloud, and Microsoft Azure, are establishing data centers in the region to meet demand and adhere to data residency regulations.

Cybersecurity Investments: As digital adoption rises, so do cybersecurity concerns. Cyber threats, including ransomware and data breaches, have driven enterprises to invest heavily in advanced cybersecurity solutions, such as multi-factor authentication (MFA), endpoint protection, and network security.

Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are integral to the Saudi ICT strategy, with applications ranging from customer service automation to predictive analytics. Industries such as finance, healthcare, and logistics leverage AI for personalized customer experiences, risk assessment, and operational efficiency.

Internet of Things (IoT) and Smart Cities: IoT is gaining momentum in Saudi Arabia, particularly in the context of smart city projects like NEOM and the Red Sea Project. Investments in IoT devices and infrastructure facilitate real-time monitoring, efficient energy management, and connected transport systems.

5G and Telecommunications: The deployment of 5G technology across Saudi Arabia has been a game-changer, enabling high-speed connectivity and facilitating the development of IoT, autonomous vehicles, and smart city projects. Telecom providers such as Saudi Telecom Company (STC), Mobily, and Zain are rapidly expanding 5G coverage.

3. Challenges

Data Privacy and Regulatory Compliance: With an increase in digital services, enterprises face regulatory challenges concerning data privacy and protection, especially in sectors like banking, healthcare, and government. Adhering to local and international standards while ensuring data residency within Saudi Arabia adds complexity to ICT investments.

Skills Gap and Talent Shortage: The rapid growth in ICT demand has led to a skills gap, particularly in areas like cybersecurity, AI, and cloud computing. While the government has launched initiatives to address this gap, enterprises still face challenges in recruiting skilled professionals locally.

High Initial Costs of Advanced Technologies: The upfront costs of adopting technologies like AI, IoT, and 5G can be prohibitive for small and medium-sized enterprises (SMEs). Despite government support, smaller firms often struggle to keep pace with larger players in terms of digital investment.

4. Key Sectors Driving ICT Investment

Banking, Financial Services, and Insurance (BFSI): The BFSI sector is a major investor in ICT, focusing on digital payment solutions, cybersecurity, and regulatory technology. Banks and financial institutions are also leveraging AI for fraud detection, customer insights, and enhanced service delivery.

Healthcare: Healthcare has become a priority sector, with substantial investments in telemedicine, electronic health records (EHR), and AI-driven diagnostics. ICT investments in healthcare aim to improve service accessibility, patient outcomes, and operational efficiency.

Retail and E-commerce: The e-commerce boom has driven ICT investments in the retail sector, with a focus on customer experience, supply chain optimization, and secure payment gateways. Enterprises invest in analytics, AI, and CRM systems to enhance customer engagement and personalize marketing efforts.

Oil and Gas: As Saudi Arabia’s primary industry, oil and gas companies are investing in IoT, AI, and analytics to improve operational efficiency, safety, and asset management. Digital solutions are particularly important in predictive maintenance and real-time monitoring.

Education: With the increasing demand for online education and e-learning platforms, the education sector has seen growing ICT investments. The focus here is on virtual classrooms, learning management systems (LMS), and data analytics for personalized learning experiences.

5. Key Players in the Saudi ICT Market

Saudi Telecom Company (STC): STC is a major player in the telecommunications sector, leading in the rollout of 5G and cloud services. It has also expanded its portfolio to include data centers and cybersecurity services.

Advanced Electronics Company (AEC): AEC provides ICT solutions in defense, healthcare, and energy sectors, focusing on digital transformation solutions tailored to Saudi Arabia’s unique needs.

SAP: SAP is a prominent player in enterprise software and cloud solutions, catering to industries ranging from healthcare to oil and gas. SAP has partnered with the government to drive digital initiatives in line with Vision 2030.

Oracle: Oracle is a key provider of cloud and database solutions in Saudi Arabia, especially popular in sectors like BFSI, healthcare, and education. Oracle has also invested in local data centers to comply with data residency requirements.

IBM and Microsoft: Both companies have a strong presence in the Saudi ICT market, offering cloud, AI, and cybersecurity solutions to enterprises. IBM has been involved in smart city projects, while Microsoft supports cloud adoption and digital transformation initiatives.

6. Future Outlook and Opportunities

Expansion of Cloud Infrastructure: With demand for data storage and processing on the rise, there is potential for further expansion in cloud infrastructure. As more companies adopt cloud-native solutions, local data centers will likely proliferate to meet regulatory and performance requirements.

Growing Demand for Cybersecurity: Given the increase in cyber threats, investment in cybersecurity solutions will continue to grow. There is a significant opportunity for companies offering advanced threat detection, identity and access management, and compliance solutions.

AI and Automation in Public and Private Sectors: The Saudi government’s push for AI development under Vision 2030 is encouraging enterprises across sectors to adopt AI for various use cases. With the establishment of the Saudi Data and Artificial Intelligence Authority (SDAIA), AI adoption is expected to rise in areas like healthcare, transportation, and finance.

IoT in Manufacturing and Energy: IoT technology is expected to further penetrate sectors like manufacturing and energy, where real-time monitoring and predictive maintenance are crucial. Investment in IoT infrastructure will be essential for achieving Vision 2030 goals, especially in the context of smart cities and the energy sector.

Conclusion

The ICT investment landscape in Saudi Arabia is rapidly advancing, fueled by government-led initiatives, increased digital adoption, and technological advancements. Key players such as STC, SAP, Oracle, and Microsoft are well-positioned to leverage the growing demand for cloud, AI, cybersecurity, and IoT solutions. Although challenges like the skills gap and data privacy concerns persist, the market’s future is promising as enterprises invest in digital solutions that align with the Vision 2030 strategy. With continued support from the government and the private sector, Saudi Arabia’s ICT investment market is set to grow significantly in the coming years, supporting the nation’s transition to a diversified, knowledge-driven economy.