#Kubernetes For Machine Learning

[enterprise-cloud-services]

Understand how Generative AI is accelerating Kubernetes adoption, shaping industries with scalable, automated, and innovative approaches.

[dieterziegler159]

How Is Gen AI Driving Kubernetes Demand Across Industries?

Understand how Generative AI is accelerating Kubernetes adoption, shaping industries with scalable, automated, and innovative approaches. A new breakthrough in AI, called generative AI or Gen AI, is creating incredible waves across industries and beyond. With this technology rapidly evolving there is growing pressure on the available structure to support both the deployment and scalability of…

[generative-ai-in-bi]

How Is Gen AI Driving Kubernetes Demand Across Industries?

Unveil how Gen AI is pushing Kubernetes to the forefront, delivering industry-specific solutions with precision and scalability.

Original Source: https://bit.ly/4cPS7G0

A new breakthrough in AI, called generative AI or Gen AI, is creating incredible waves across industries and beyond. With this technology rapidly evolving there is growing pressure on the available structure to support both the deployment and scalability of the technology. Kubernetes, an effective container orchestration platform is already indicating its ability as one of the enablers in this context. This article critically analyzes how Generative AI gives rise to the use of Kubernetes across industries with a focus of the coexistence of these two modern technological forces.

The Rise of Generative AI and Its Impact on Technology

Machine learning has grown phenomenally over the years and is now foundational in various industries including healthcare, banking, production as well as media and entertainment industries. This technology whereby an AI model is trained to write, design or even solve business problems is changing how business is done. Gen AI’s capacity to generate new data and solutions independently has opened opportunities for advancements as has never been seen before.

If companies are adopting Generative AI , then the next big issue that they are going to meet is on scalability of models and its implementation. These resource- intensive applications present a major challenge to the traditional IT architectures. It is here that Kubernetes comes into the picture, which provides solutions to automate deployment, scaling and managing the containerised applications. Kubernetes may be deployed to facilitate the ML and deep learning processing hence maximizing the efficiency of the AI pipeline to support the future growth of Gen AI applications.

The Intersection of Generative AI and Kubernetes

The integration of Generative AI and Kubernetes is probably the most significant traffic in the development of AI deployment approaches. Kubernetes is perfect for the dynamics of AI workloads in terms of scalability and flexibility. The computation of Gen AI models demands considerable resources, and Kubernetes has all the tools required to properly orchestrate those resources for deploying AI models in different setups.

Kubernetes’ infrastructure is especially beneficial for AI startups and companies that plan to use Generative AI. It enables the decentralization of workload among several nodes so that training, testing, and deployment of AI models are highly distributed. This capability is especially important for businesses that require to constantly revolve their models to adapt to competition. In addition, Kubernetes has direct support for GPU, which helps in evenly distributing computational intensity that comes with deep learning workloads thereby making it perfect for AI projects.

Key Kubernetes Features that Enable Efficient Generative AI Deployment

Scalability:

Kubernetes excels at all levels but most notably where applications are scaled horizontally. Especially for Generative AI which often needs a lot of computation, Kubernetes is capable of scaling the pods, the instances of the running processes and provide necessary resources for the workload claims without having any human intervention.

Resource Management:

Effort is required to be allocated efficiently so as to perform the AI workloads. Kubernetes assists in deploying as well as allocating resources within the cluster from where the AI models usually operate while ensuring that resource consumption and distribution is efficiently controlled.

Continuous Deployment and Integration (CI/CD):

Kubernetes allows for the execution of CI CD pipelines which facilitate contingency integration as well as contingency deployment of models. This is essential for enterprises and the AI startups that use the flexibility of launching different AI solutions depending on the current needs of their companies.

GPU Support:

Kubernetes also features the support of the GPUs for the applications in deep learning from scratch that enhances the rate of training and inference of the models of AI. It is particularly helpful for AI applications that require more data processing, such as image and speech recognition.

Multi-Cloud and Hybrid Cloud Support:

The fact that the Kubernetes can operate in several cloud environment and on-premise data centers makes it versatile as AI deployment tool. It will benefit organizations that need a half and half cloud solution and organizations that do not want to be trapped in the web of the specific company.

Challenges of Running Generative AI on Kubernetes

Complexity of Setup and Management:

That aid Kubernetes provides a great platform for AI deployments comes at the cost of operational overhead. Deploying and configuring a Kubernetes Cluster for AI based workloads therefore necessitates knowledge of both Kubernetes and the approach used to develop these models. This could be an issue for organizations that are not able to gather or hire the required expertise.

Resource Constraints:

Generative AI models require a lot of computing power and when running them in a Kubernetes environment, the computational resources can be fully utilised. AI works best when the organizational resources are well managed to ensure that there are no constraints in the delivery of the application services.

Security Concerns:

Like it is the case with any cloud-native application, security is a big issue when it comes to running artificial intelligence models on Kubernetes. Security of the data and models that AI employs needs to be protected hence comes the policies of encryption, access control and monitoring.

Data Management:

Generative AI models make use of multiple dataset samples for its learning process and is hard to deal with the concept in Kubernetes. Managing these datasets as well as accessing and processing them in a manner that does not hinder the overall performance of an organization is often a difficult task.

Conclusion: The Future of Generative AI is Powered by Kubernetes

As Generative AI advances and integrates into many sectors, the Kubernetes efficient and scalable solutions will only see a higher adoption rate. Kubernetes is a feature of AI architectures that offer resources and facilities for the development and management of AI model deployment.

If you’re an organization planning on putting Generative AI to its best use, then adopting Kubernetes is non-negotiable. Mounting the AI workloads, utilizing the resources in the best possible manner, and maintaining the neat compatibility across the multiple and different clouds are some of the key solutions provided by Kubernetes for the deployment of the AI models. With continued integration between Generative AI and Kubernetes, we have to wonder what new and exciting uses and creations are yet to come, thus strengthening Kubernetes’ position as the backbone for enterprise AI with Kubernetes. The future is bright that Kubernetes is playing leading role in this exciting technological revolution of AI.

Original Source: https://bit.ly/4cPS7G0

[qwikskills]

Most in-demand Azure cloud skills that IT professionals should have

In today’s competitive job market, having the right skillset is key to success. Azure cloud skills are becoming increasingly important for IT professionals and developers. With the help of Azure cloud services, organizations can build secure and reliable applications that can scale up or down as needed. Azure cloud skills can be used to develop applications that run on the cloud, manage data, automate processes and deploy solutions quickly and efficiently. It also allows developers to create hybrid solutions by combining on-premise resources with public cloud offerings. Having the right Azure cloud skills can open up new opportunities for IT professionals in terms of career growth and salary potential. It is essential for IT professionals to stay up-to-date with the latest developments in this field in order to remain competitive in this ever-evolving job market. The following are some of the most in-demand Azure cloud skills that IT professionals should have:

1. Azure cloud services: This includes developing applications that run on Microsoft Azure and managing data, automating processes and deploying solutions quickly and efficiently.

2. Azure IaaS: Determines how an organization can build secure, reliable applications by using public cloud resources in combination with on-premise resources to create hybrid solutions for maximum efficiency.

3. Windows Server: In order to develop solutions for either public or private clouds, developers need familiarity with this server operating system which includes Microsoft Azure hybrid solutions.

4. Windows: Determines how an organization can harness the power of the cloud by leveraging the powerful development toolset for this platform and its wide range of applications.

[atozofsoftwareengineering]

Google Cloud Platform: A Comprehensive Comparison with AWS and Azure

Unlock the power of Google Cloud Platform 🚀 Explore superior BigQuery analytics, advanced networking, and seamless hybrid cloud with Anthos. #GCP #CloudComputing #TechInnovation

The landscape of cloud computing is a constantly evolving and competitive one, with various platforms vying for attention and dominance. Among these contenders, Google Cloud Platform (GCP) stands out for its unique blend of innovative features and services. This distinguished platform offers a wealth of capabilities that set it apart from industry giants like AWS and Azure. This blog seeks to…

[coreai-5]

Visit here learn these Tools - Online or Offline

[jannah-software]

Developer Environment Presentation 1 Part 2: Generate Bootstrap Configuration Files, and Uninstall Previous Jannah Installation

From cloning the operator from github.com. To generating new Molecule configuration, environment variable files, for Jannah deployment. And uninstall previous Jannah installation.

Video Highlights Purpose is to showcase the developer environment (day to day developer experience). I performed the following steps: Clone the operator code base, from git. git clone https://github.com/jannahio/operator; Change into the cloned operator directory: cd operator; Operator configuration needs environment variables file to bootstrap. So copied environment variables file into…

[advisedskills]

Future of DevOps in 2023 and beyond

DevOps has rapidly become a critical part of software development in recent years, providing a seamless process that allows development and operations teams to work together seamlessly to deliver quality software faster.

As we look ahead to 2023 and beyond, the future of DevOps continues to evolve and become more complex, as technology and processes continue to advance. In this article, we’ll explore the latest trends and innovations in DevOps, and what they mean for the future of software development.

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) have already begun to play a big role in DevOps, and they will continue to do so in the future. AI and ML are already being used in DevOps to automate repetitive tasks, monitor applications, and identify performance issues. In the future, we can expect to see even more sophisticated AI and ML systems that will be able to analyze and predict software behavior, detect security vulnerabilities, and respond to incidents automatically.

This will lead to faster and more efficient software development, freeing up developers to focus on more strategic work, such as improving software performance and addressing customer needs. Additionally, AI and ML will help organizations to identify and resolve problems before they occur, reducing downtime and minimizing the impact of outages.

Containers and Kubernetes

Containers and Kubernetes have rapidly become an essential part of DevOps, allowing teams to easily deploy, scale, and manage applications. In the future, we can expect to see more organizations adopt containers and Kubernetes, as they continue to provide a more efficient and scalable solution compared to traditional virtualization methods.

Additionally, as the number of containers and microservices grows, Kubernetes will play a crucial role in managing the deployment and scaling of these applications. In the future, we can expect to see the development of more advanced Kubernetes tools and platforms, allowing organizations to manage large-scale container deployments with ease.

Cloud Computing

Cloud computing has already had a significant impact on DevOps, allowing organizations to quickly and easily scale their infrastructure and resources as needed. In the future, we can expect to see even more organizations adopt cloud computing, as they continue to offer a more flexible, cost-effective, and scalable solution compared to traditional on-premise solutions.

Additionally, we can expect to see the development of new cloud computing services and technologies that will further improve the efficiency and speed of DevOps. For example, the use of serverless computing and edge computing will allow organizations to run applications and services closer to the edge, reducing latency and improving performance.

Collaboration and Communication

Collaboration and communication are key to the success of DevOps, and in the future, we can expect to see even more emphasis placed on these areas. As software development becomes more complex and global, organizations will need to improve their collaboration and communication processes to ensure that development and operations teams are working together effectively.

In the future, we can expect to see the development of new collaboration and communication tools and platforms, such as chatbots and virtual reality, that will allow teams to work together more efficiently and effectively. Additionally, we can expect to see the integration of DevOps tools and platforms with existing enterprise systems, such as CRM and ERP, to improve communication and collaboration across the organization.

Conclusion

In conclusion, the future of DevOps is bright, with many exciting new technologies and trends emerging. As technology and processes continue to advance, we can expect to see even more sophisticated DevOps systems that will allow organizations to deliver high-quality software faster and more efficiently.

However, while technology is important, it is also crucial that organizations focus on improving collaboration.

[pressplaytech]

Is cPanel on Its Deathbed? A Tale of Technology, Profits, and a Slow-Moving Train Wreck

Ah, cPanel. The go-to control panel for many web hosting services since the dawn of, well, web hosting. Once the epitome of innovation, it’s now akin to a grizzled war veteran, limping along with a cane and wearing an “I Survived Y2K” t-shirt. So what went wrong? Let’s dive into this slow-moving technological telenovela, rife with corporate greed, security loopholes, and a legacy that may be hanging by a thread.

Chapter 1: A Brief, Glorious History (Or How cPanel Shot to Stardom)

Once upon a time, cPanel was the bee’s knees. Launched in 1996, this software was, for a while, the pinnacle of web management systems. It promised simplicity, reliability, and functionality. Oh, the golden years!

Chapter 2: The Tech Stack Tortoise

In the fast-paced world of technology, being stagnant is synonymous with being extinct. While newer tech stacks are integrating AI, machine learning, and all sorts of jazzy things, cPanel seems to be stuck in a time warp. Why? Because the tech stack is more outdated than a pair of bell-bottom trousers. No Docker, no Kubernetes, and don’t even get me started on the lack of robust API support.

Chapter 3: “The Corpulent Corporate”

In 2018, Oakley Capital, a private equity firm, acquired cPanel. For many, this was the beginning of the end. Pricing structures were jumbled, turning into a monetisation extravaganza. It’s like turning your grandma’s humble pie shop into a mass production line for rubbery, soulless pies. They’ve squeezed every ounce of profit from it, often at the expense of the end-users and smaller hosting companies.

Chapter 4: Security—or the Lack Thereof

Ah, the elephant in the room. cPanel has had its fair share of vulnerabilities. Whether it’s SQL injection flaws, privilege escalation, or simple, plain-text passwords (yes, you heard right), cPanel often appears in the headlines for all the wrong reasons. It’s like that dodgy uncle at family reunions who always manages to spill wine on the carpet; you know he’s going to mess up, yet somehow he’s always invited.

Chapter 5: The (Dis)loyal Subjects—The Hosting Companies

Remember those hosting companies that once swore by cPanel? Well, let’s just say some of them have been seen flirting with competitors at the bar. Newer, shinier control panels are coming to market, offering modern tech stacks and, gasp, lower prices! It’s like watching cPanel’s loyal subjects slowly turn their backs, one by one.

Chapter 6: The Alternatives—Not Just a Rebellion, but a Revolution

Plesk, Webmin, DirectAdmin, oh my! New players are rising, offering updated tech stacks, more customizable APIs, and—wait for it—better security protocols. They’re the Han Solos to cPanel’s Jabba the Hutt: faster, sleeker, and without the constant drooling.

Conclusion: The Twilight Years or a Second Wind?

The debate rages on. Is cPanel merely an ageing actor waiting for its swan song, or can it adapt and evolve, perhaps surprising us all? Either way, the story of cPanel serves as a cautionary tale: adapt or die. And for heaven’s sake, update your tech stack before it becomes a relic in a technology museum, right between floppy disks and dial-up modems.

This outline only scratches the surface, but it’s a start. If cPanel wants to avoid becoming the Betamax of web management systems, it better start evolving—stat. Cheers!

[priya-joshi]

Elevating Your Full-Stack Developer Expertise: Exploring Emerging Skills and Technologies

Introduction: In the dynamic landscape of web development, staying at the forefront requires continuous learning and adaptation. Full-stack developers play a pivotal role in crafting modern web applications, balancing frontend finesse with backend robustness. This guide delves into the evolving skills and technologies that can propel full-stack developers to new heights of expertise and innovation.

Pioneering Progress: Key Skills for Full-Stack Developers

1. Innovating with Microservices Architecture:

Microservices have redefined application development, offering scalability and flexibility in the face of complexity. Mastery of frameworks like Kubernetes and Docker empowers developers to architect, deploy, and manage microservices efficiently. By breaking down monolithic applications into modular components, developers can iterate rapidly and respond to changing requirements with agility.

2. Embracing Serverless Computing:

The advent of serverless architecture has revolutionized infrastructure management, freeing developers from the burdens of server maintenance. Platforms such as AWS Lambda and Azure Functions enable developers to focus solely on code development, driving efficiency and cost-effectiveness. Embrace serverless computing to build scalable, event-driven applications that adapt seamlessly to fluctuating workloads.

3. Crafting Progressive Web Experiences (PWEs):

Progressive Web Apps (PWAs) herald a new era of web development, delivering native app-like experiences within the browser. Harness the power of technologies like Service Workers and Web App Manifests to create PWAs that are fast, reliable, and engaging. With features like offline functionality and push notifications, PWAs blur the lines between web and mobile, captivating users and enhancing engagement.

4. Harnessing GraphQL for Flexible Data Management:

GraphQL has emerged as a versatile alternative to RESTful APIs, offering a unified interface for data fetching and manipulation. Dive into GraphQL's intuitive query language and schema-driven approach to simplify data interactions and optimize performance. With GraphQL, developers can fetch precisely the data they need, minimizing overhead and maximizing efficiency.

5. Unlocking Potential with Jamstack Development:

Jamstack architecture empowers developers to build fast, secure, and scalable web applications using modern tools and practices. Explore frameworks like Gatsby and Next.js to leverage pre-rendering, serverless functions, and CDN caching. By decoupling frontend presentation from backend logic, Jamstack enables developers to deliver blazing-fast experiences that delight users and drive engagement.

6. Integrating Headless CMS for Content Flexibility:

Headless CMS platforms offer developers unprecedented control over content management, enabling seamless integration with frontend frameworks. Explore platforms like Contentful and Strapi to decouple content creation from presentation, facilitating dynamic and personalized experiences across channels. With headless CMS, developers can iterate quickly and deliver content-driven applications with ease.

7. Optimizing Single Page Applications (SPAs) for Performance:

Single Page Applications (SPAs) provide immersive user experiences but require careful optimization to ensure performance and responsiveness. Implement techniques like lazy loading and server-side rendering to minimize load times and enhance interactivity. By optimizing resource delivery and prioritizing critical content, developers can create SPAs that deliver a seamless and engaging user experience.

8. Infusing Intelligence with Machine Learning and AI:

Machine learning and artificial intelligence open new frontiers for full-stack developers, enabling intelligent features and personalized experiences. Dive into frameworks like TensorFlow.js and PyTorch.js to build recommendation systems, predictive analytics, and natural language processing capabilities. By harnessing the power of machine learning, developers can create smarter, more adaptive applications that anticipate user needs and preferences.

9. Safeguarding Applications with Cybersecurity Best Practices:

As cyber threats continue to evolve, cybersecurity remains a critical concern for developers and organizations alike. Stay informed about common vulnerabilities and adhere to best practices for securing applications and user data. By implementing robust security measures and proactive monitoring, developers can protect against potential threats and safeguard the integrity of their applications.

10. Streamlining Development with CI/CD Pipelines:

Continuous Integration and Deployment (CI/CD) pipelines are essential for accelerating development workflows and ensuring code quality and reliability. Explore tools like Jenkins, CircleCI, and GitLab CI/CD to automate testing, integration, and deployment processes. By embracing CI/CD best practices, developers can deliver updates and features with confidence, driving innovation and agility in their development cycles.

[enterprise-cloud-services]

Unveil how Gen AI is pushing Kubernetes to the forefront, delivering industry-specific solutions with precision and scalability.

[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.

[korshubudemycoursesblog]

What Is Jenkins, and Why Should You Care?

Before diving into the course itself, let’s take a moment to understand Jenkins and why it’s so crucial in today’s software development ecosystem. Jenkins is an open-source automation tool that plays a pivotal role in DevOps practices, specifically in building CI/CD pipelines. But what exactly does that mean?

CI/CD, which stands for Continuous Integration and Continuous Delivery, is the process that allows developers to continuously merge code and automate the testing, building, and deployment processes. This is where Jenkins shines. It automates these workflows, ensuring faster deployment, improved code quality, and most importantly, more efficient teams.

For anyone serious about a career in DevOps, mastering Jenkins is non-negotiable. It’s the tool that brings together all the moving parts of software development and delivery, ensuring smooth transitions from one stage to the next.

Why Enroll in The Complete Jenkins DevOps CI/CD Pipeline Bootcamp?

So, what makes The Complete Jenkins DevOps CI/CD Pipeline Bootcamp stand out from other online courses? It’s simple – this course is built to take you from beginner to advanced, making it ideal for both newcomers and those who already have some experience but are looking to solidify their knowledge.

Here are some reasons why this course is worth your time:

Comprehensive Curriculum: You’ll learn everything from setting up Jenkins to building and managing complex CI/CD pipelines. This isn’t just a basic introduction; it’s a deep dive into everything Jenkins can do.

Hands-On Projects: The best way to learn is by doing, and this course is packed with hands-on projects. You’ll get real-world experience setting up Jenkins pipelines, integrating tools like Git, Docker, and Kubernetes, and automating tasks that would otherwise be time-consuming.

Career Growth: DevOps professionals are in high demand. Completing this course and mastering Jenkins will set you apart from other job seekers, increasing your chances of landing a high-paying role in a top tech company.

What Will You Learn in The Complete Jenkins DevOps CI/CD Pipeline Bootcamp?

Here’s a breakdown of what you’ll cover in this course:

1. Jenkins Installation and Setup

The first step is getting Jenkins up and running. You’ll learn how to install Jenkins on your local machine, configure it for different environments, and get a solid grasp of its user interface.

2. Jenkins Plugins

One of Jenkins’ greatest strengths is its flexibility, thanks to the vast library of plugins available. You’ll explore essential plugins for CI/CD pipelines, version control systems like Git, and integration with tools like Docker and Kubernetes.

3. Building Your First Jenkins Pipeline

Once you’ve set up Jenkins and configured the necessary plugins, it’s time to build your first pipeline. This is where Jenkins automates the process of integrating code from various developers, testing it, and deploying it to production.

4. Integrating Jenkins with Git

Jenkins and Git are a match made in DevOps heaven. Git serves as your version control system, and Jenkins automates the process of pulling code, running tests, and building applications. In this section, you’ll learn how to integrate Jenkins with Git for a seamless CI/CD pipeline.

5. Docker and Jenkins: A Perfect Combination

Containerization has become a key part of modern software development, and Docker is leading the way. In this course, you’ll learn how to integrate Docker with Jenkins to create containerized applications and deploy them efficiently.

6. Automating Tests with Jenkins

One of the main benefits of CI/CD pipelines is the ability to automate tests. Jenkins makes this process incredibly simple. You’ll learn how to configure automated testing within your pipeline, ensuring that only high-quality code makes it to production.

7. Deploying with Kubernetes

As cloud-native applications become more popular, Kubernetes has emerged as the go-to solution for managing containers. You’ll discover how to deploy Jenkins pipelines on Kubernetes, taking your DevOps skills to the next level.

8. Monitoring and Scaling Jenkins Pipelines

Once you’ve set up a pipeline, the work doesn’t stop there. Jenkins pipelines need to be monitored and scaled according to the needs of your project. You’ll explore best practices for monitoring pipelines and scaling Jenkins to handle larger workloads.

How Can Free AI Help You in DevOps?

As AI continues to evolve, it’s becoming an essential tool in virtually every industry, and DevOps is no exception. There are several free AI tools and platforms that can enhance your Jenkins experience by automating tasks, predicting failures in your pipeline, and even optimizing code. AI can help you identify potential bottlenecks in your pipeline before they become significant issues. Some tools even suggest improvements to your configurations, making your pipeline more efficient over time.

Using AI tools alongside Jenkins is a game-changer for developers and DevOps professionals. Not only does it streamline your workflow, but it also helps you deliver better-quality products faster.

Why CI/CD Pipelines Are the Future of Software Development

In today’s fast-paced software development environment, the traditional methods of development and deployment just don’t cut it anymore. Companies need faster, more efficient ways to deliver updates and new features, and that’s where CI/CD pipelines come in.

By automating the process of building, testing, and deploying code, CI/CD pipelines significantly reduce the time it takes to get from development to production. More importantly, they ensure that the code being deployed is of the highest quality, reducing the risk of bugs and errors making it into production.

Who Should Take The Complete Jenkins DevOps CI/CD Pipeline Bootcamp?

This course is perfect for:

Aspiring DevOps Engineers: If you’re looking to break into the field, this course will give you the foundational skills you need to succeed.

Developers Looking to Automate: If you’re a developer tired of repetitive tasks, learning Jenkins will free up your time to focus on more critical aspects of development.

Project Managers and Tech Leads: Understanding how CI/CD pipelines work is essential for managing modern software projects.

Why Now Is the Perfect Time to Master Jenkins

The demand for DevOps professionals has never been higher, and companies are looking for individuals who can not only manage CI/CD pipelines but also bring new ideas and tools to the table. By completing The Complete Jenkins DevOps CI/CD Pipeline Bootcamp, you’ll be equipped with the knowledge and experience to stand out in a crowded job market.

Moreover, the integration of free AI tools into DevOps workflows is becoming more prevalent. By learning both Jenkins and these cutting-edge AI tools, you’ll stay ahead of the curve and ensure you’re using the latest technologies to your advantage.

Conclusion

In summary, The Complete Jenkins DevOps CI/CD Pipeline Bootcamp is your one-stop shop for mastering the key skills needed in today’s DevOps world. From setting up Jenkins and building pipelines to integrating with Git, Docker, and Kubernetes, this course covers it all. Plus, you’ll gain valuable insights into how free AI tools can enhance your DevOps workflows.

By the end of the course, you’ll be ready to build, test, and deploy software more efficiently than ever before. Whether you’re just starting out or looking to advance your career, this course is the perfect way to take your DevOps skills to the next level.

Now’s the time to dive into the world of Jenkins and CI/CD pipelines. Ready to accelerate your career? The Complete Jenkins DevOps CI/CD Pipeline Bootcamp is here to help you achieve that goal!

[govindhtech]

New GKE Ray Operator on Kubernetes Engine Boost Ray Output

GKE Ray Operator

The field of AI is always changing. Larger and more complicated models are the result of recent advances in generative AI in particular, which forces businesses to efficiently divide work among more machines. Utilizing Google Kubernetes Engine (GKE), Google Cloud’s managed container orchestration service, in conjunction with ray.io, an open-source platform for distributed AI/ML workloads, is one effective strategy. You can now enable declarative APIs to manage Ray clusters on GKE with a single configuration option, making that pattern incredibly simple to implement!

Ray offers a straightforward API for smoothly distributing and parallelizing machine learning activities, while GKE offers an adaptable and scalable infrastructure platform that streamlines resource management and application management. For creating, implementing, and maintaining Ray applications, GKE and Ray work together to provide scalability, fault tolerance, and user-friendliness. Moreover, the integrated Ray Operator on GKE streamlines the initial configuration and directs customers toward optimal procedures for utilizing Ray in a production setting. Its integrated support for cloud logging and cloud monitoring improves the observability of your Ray applications on GKE, and it is designed with day-2 operations in mind.

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Getting started

When establishing a new GKE Cluster in the Google Cloud dashboard, make sure to check the “Enable Ray Operator” function. This is located under “AI and Machine Learning” under “Advanced Settings” on a GKE Autopilot Cluster.

The Enable Ray Operator feature checkbox is located under “AI and Machine Learning” in the “Features” menu of a Standard Cluster.

You can set an addons flag in the following ways to utilize the gcloud CLI:

gcloud container clusters create CLUSTER_NAME \ — cluster-version=VERSION \ — addons=RayOperator

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GKE hosts and controls the Ray Operator on your behalf after it is enabled. After a cluster is created, your cluster will be prepared to run Ray applications and build other Ray clusters.

Record-keeping and observation

When implementing Ray in a production environment, efficient logging and metrics are crucial. Optional capabilities of the GKE Ray Operator allow for the automated gathering of logs and data, which are then seamlessly stored in Cloud Logging and Cloud Monitoring for convenient access and analysis.

When log collection is enabled, all logs from the Ray cluster Head node and Worker nodes are automatically collected and saved in Cloud Logging. The generated logs are kept safe and easily accessible even in the event of an unintentional or intentional shutdown of the Ray cluster thanks to this functionality, which centralizes log aggregation across all of your Ray clusters.

By using Managed Service for Prometheus, GKE may enable metrics collection and capture all system metrics exported by Ray. System metrics are essential for tracking the effectiveness of your resources and promptly finding problems. This thorough visibility is especially important when working with costly hardware like GPUs. You can easily construct dashboards and set up alerts with Cloud Monitoring, which will keep you updated on the condition of your Ray resources.

TPU assistance

Large machine learning model training and inference are significantly accelerated using Tensor Processing Units (TPUs), which are custom-built hardware accelerators. Ray and TPUs may be easily used with its AI Hypercomputer architecture to scale your high-performance ML applications with ease.

By adding the required TPU environment variables for frameworks like JAX and controlling admission webhooks for TPU Pod scheduling, the GKE Ray Operator simplifies TPU integration. Additionally, autoscaling for Ray clusters with one host or many hosts is supported.

Reduce the delay at startup

When operating AI workloads in production, it is imperative to minimize start-up delay in order to maximize the utilization of expensive hardware accelerators and ensure availability. When used with other GKE functions, the GKE Ray Operator can significantly shorten this startup time.

You can achieve significant speed gains in pulling images for your Ray clusters by hosting your Ray images on Artifact Registry and turning on image streaming. Huge dependencies, which are frequently required for machine learning, can lead to large, cumbersome container images that take a long time to pull. For additional information, see Use Image streaming to pull container images. Image streaming can drastically reduce this image pull time.

Moreover, model weights or container images can be preloaded onto new nodes using GKE secondary boot drives. When paired with picture streaming, this feature can let your Ray apps launch up to 29 times faster, making better use of your hardware accelerators.

Scale Ray is currently being produced

A platform that grows with your workloads and provides a simplified Pythonic experience that your AI developers are accustomed to is necessary to stay up with the quick advances in AI. This potent trifecta of usability, scalability, and dependability is delivered by Ray on GKE. It’s now simpler than ever to get started and put best practices for growing Ray in production into reality with the GKE Ray Operator.

Read more on govindhtech.com

[ahansaxena12]

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.

[qcs01]

Red Hat Training Categories: Empowering IT Professionals for the Future

Red Hat, a leading provider of enterprise open-source solutions, offers a comprehensive range of training programs designed to equip IT professionals with the knowledge and skills needed to excel in the rapidly evolving world of technology. Whether you're an aspiring system administrator, a seasoned DevOps engineer, or a cloud architect, Red Hat's training programs cover key technologies and tools that drive modern IT infrastructures. Let’s explore some of the key Red Hat training categories.

1. Red Hat Enterprise Linux (RHEL)

RHEL is the foundation of many enterprises, and Red Hat offers extensive training to help IT professionals master Linux system administration, automation, and security. Key courses in this category include:

Red Hat Certified System Administrator (RHCSA): An essential certification for beginners in Linux administration.

Red Hat Certified Engineer (RHCE): Advanced training in system administration, emphasizing automation using Ansible.

Security and Identity Management: Focuses on securing Linux environments and managing user identities.

2. Ansible Automation

Automation is at the heart of efficient IT operations, and Ansible is a powerful tool for automating tasks across diverse environments. Red Hat offers training on:

Ansible Basics: Ideal for beginners looking to understand how to automate workflows and deploy applications.

Advanced Ansible Automation: Focuses on optimizing playbooks, integrating Ansible Tower, and managing large-scale deployments.

3. OpenShift Container Platform

OpenShift is Red Hat’s Kubernetes-based platform for managing containerized applications. Red Hat training covers topics like:

OpenShift Administration: Learn how to install, configure, and manage OpenShift clusters.

OpenShift Developer: Build, deploy, and scale containerized applications on OpenShift.

4. Red Hat Cloud Technologies

With businesses rapidly adopting cloud technologies, Red Hat’s cloud training programs ensure that professionals are prepared for cloud-native development and infrastructure management. Key topics include:

Red Hat OpenStack: Learn how to deploy and manage private cloud environments.

Red Hat Virtualization: Master the deployment of virtual machines and manage large virtualized environments.

5. DevOps Training

Red Hat is committed to promoting DevOps practices, helping teams collaborate more efficiently. DevOps training includes:

Red Hat DevOps Pipelines and CI/CD: Learn how to streamline software development, testing, and deployment processes.

Container Development and Kubernetes Integration: Get hands-on experience with containerized applications and orchestrating them using Kubernetes.

6. Cloud-Native Development

As enterprises move towards microservices and cloud-native applications, Red Hat provides training on developing scalable and resilient applications:

Microservices Architecture: Learn to build and deploy microservices using Red Hat’s enterprise open-source tools.

Serverless Application Development: Focus on building lightweight applications that scale on demand.

7. Red Hat Satellite

Red Hat Satellite simplifies Linux system management at scale, and its training focuses on:

Satellite Server Administration: Learn how to automate system maintenance and streamline software updates across your RHEL environment.

8. Security and Compliance

In today's IT landscape, security is paramount. Red Hat offers specialized training on securing infrastructure and ensuring compliance:

Linux Security Essentials: Learn to safeguard Linux environments from vulnerabilities.

Advanced Security Features: Cover best practices for maintaining security across hybrid cloud environments.

Why Red Hat Training?

Red Hat certifications are globally recognized, validating your expertise in open-source technologies. They offer hands-on, practical training that helps professionals apply their knowledge directly to real-world challenges. By investing in Red Hat training, you are preparing yourself for future innovations and ensuring that your skills remain relevant in an ever-changing industry.

Conclusion

Red Hat training empowers IT professionals to build, manage, and secure the enterprise-grade systems that are shaping the future of technology. Whether you're looking to enhance your Linux skills, dive into automation with Ansible, or embrace cloud-native development, there’s a Red Hat training category tailored to your needs.

For more details click www.hawkstack.com