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qbokubernetesengine · 1 year ago

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What is Kubeflow and How to Deploy it on Kubernetes

Machine learning (ML) processes on Kubernetes, the top container orchestration technology, may be simplified and streamlined with Kubeflow, an open-source platform. From data pretreatment to model deployment, it's like having your specialised toolbox for managing all your ML and AI operations within the Kubernetes ecosystem. Keep on reading this article to know about Kubeflow deployment in Kubernetes.

Why Kubeflow?

Integrated Approach

Complex ML processes can more easily be managed with Kubeflow because it unifies several tools and components into a unified ecosystem.

Efficiency in scaling

Thanks to its foundation in Kubernetes, Kubeflow can easily grow to manage massive datasets and ML tasks that require a lot of computing power.

Consistent results

The significance of reproducibility is highlighted by Kubeflow, who defines ML workflows as code, allowing for the replication and tracking of experiments.

Maximising the use of available resources

Separating ML workloads inside Kubernetes eliminates resource conflicts and makes sure everything runs well.

Easy Implementation

Kubeflow deployment in Kubernetes makes deploying machine learning models as web services easier, which opens the door to real-time applications.

Integration of Kubeflow with Kubernetes on GCP

For this example, we will utilise Google Cloud Platform (GCP) and their managed K8s GKE. However, there may be subtle variations depending on the provider you choose. The majority of this tutorial is still applicable to you.

Set up the GCP project

Just follow these instructions for Kubeflow deployment in Kubernetes.

You can start a new project or choose one from the GCP Console.

Establish that you are the designated "owner" of the project. The implementation process involves creating various service accounts with adequate permissions to integrate with GCP services without any hitches.

Verify that your project meets all billing requirements. To make changes to a project, refer to the Billing Settings Guide.

Verify that the necessary APIs are allowed on the following GCP Console pages:

o Compute Engine API

o Kubernetes Engine API

o Identity and Access Management (IAM) API

o Deployment Manager API

o Cloud Resource Manager API

o Cloud Filestore API

o AI Platform Training & Prediction API

Remember that the default GCP version of Kubeflow cannot be run on the GCP Free Tier due to space constraints, regardless of whether you are utilising the $300 credit 12-month trial term. A payable account is where you need to be.

Deploy kubeFlow using the CLI

Before running the command line installer for Kubeflow:

Make sure you've got the necessary tools installed:

kubectl

Gcloud

Check the GCP documentation for the bare minimum requirements and ensure your project satisfies them.

Prepare your environment

So far, we've assumed you can connect to and operate a GKE cluster. If not, use one as a starting point:

Container clusters in Gcloud generate cluster-name environment compute-zone

More details regarding the same command can be found in the official documentation.

To get the Kubeflow CLI binary file, follow these instructions:

Go to the kfctl releases page and download the v1.0.2 version.

Unpack the tarball:

tar -xvf kfctl_v1.0.2_<platform>.tar.gz

• Sign in. Executing this command is mandatory just once:

gcloud auth login

• Establish login credentials. Executing this command is mandatory just once:

gcloud auth application-default login

• Set the zone and project default values in Gcloud.

To begin setting up the Kubeflow deployment, enter your GCP project ID and choose the zone:

export PROJECT=<your GCP project ID> export ZONE=<your GCP zone>

gcloud config set project ${PROJECT} gcloud config set compute/zone ${ZONE}

Select the KFDef spec to use for your deployment

Export

CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.0-branch/kfdef/kfctl_gcp_iap.v1.0.2.yaml"

Ensure you include the OAuth client ID and secret you generated earlier in your established environment variables.

export CLIENT_ID=<CLIENT_ID from OAuth page> export CLIENT_SECRET=<CLIENT_SECRET from OAuth page>

You can access the CLIENT_ID and CLIENT_SECRET in the Cloud Console by going to APIs & Services -> Credientials.

Assign a directory for your configuration and give your Kubeflow deployment the name KF_NAME.

export KF_NAME=<your choice of name for the Kubeflow deployment> export BASE_DIR=<path to a base directory> export KF_DIR=${BASE_DIR}/${KF_NAME}

When you perform the kfctl apply command, Kubeflow will be deployed with the default settings:

mkdir -p ${KF_DIR} cd ${KF_DIR} kfctl apply -V -f ${CONFIG_URI}

By default, kfctl will attempt to fill the KFDef specification with a number of values.

Conclusion Although you are now familiar with the basics of Kubeflow deployment in Kubernetes, more advanced customisations can make the process more challenging. However, many of the issues brought up by the computational demands of machine learning can be resolved with a containerised, Kubernetes-managed cloud-based machine learning workflow, such as Kubeflow. It allows for scalable access to central processing and graphics processing units, which may be automatically increased to handle spikes in computing demand.

#Kubeflow deployment in Kubernetes #Nvidia GPU deployment in Kubernetes

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krutikabhosale · 17 days ago

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Scaling Agentic AI in 2025: Unlocking Autonomous Digital Labor with Real-World Success Stories

Introduction

Agentic AI is revolutionizing industries by seamlessly integrating autonomy, adaptability, and goal-driven behavior, enabling digital systems to perform complex tasks with minimal human intervention. This article explores the evolution of Agentic AI, its integration with Generative AI, and delivers actionable insights for scaling these systems. We will examine the latest deployment strategies, best practices for scalability, and real-world case studies, including how an Agentic AI course in Mumbai with placements is shaping talent pipelines for this emerging field. Whether you are a software engineer, data scientist, or technology leader, understanding the interplay between Generative AI and Agentic AI is key to unlocking digital transformation.

The Evolution of Agentic and Generative AI in Software

AI’s evolution has moved from rule-based systems and machine learning toward today’s advanced generative models and agentic systems. Traditional AI excels in narrow, predefined tasks like image recognition but lacks flexibility for dynamic environments. Agentic AI, by contrast, introduces autonomy and continuous learning, empowering systems to adapt and optimize outcomes over time without constant human oversight.

This paradigm shift is powered by Generative AI, particularly large language models (LLMs), which provide contextual understanding and reasoning capabilities. Agentic AI systems can orchestrate multiple AI services, manage workflows, and execute decisions, making them essential for real-time, multi-faceted applications across logistics, healthcare, and customer service. The rise of agentic capabilities marks a transition from AI as a tool to AI as an autonomous digital labor force, expanding workforce definitions and operational possibilities. Professionals seeking to enter this field often consider a Generative AI and Agentic AI course to gain the necessary skills and practical experience.

Latest Frameworks, Tools, and Deployment Strategies

LLM Orchestration and Autonomous Agents

Modern Agentic AI depends on orchestrating multiple LLMs and AI components to execute complex workflows. Frameworks like LangChain, Haystack, and OpenAI’s Function Calling enable developers to build autonomous agents that chain together tasks, query databases, and interact with APIs dynamically. These frameworks support multi-turn dialogue management, contextual memory, and adaptive decision-making, critical for real-world agentic applications. For those interested in hands-on learning, enrolling in an Agentic AI course in Mumbai with placements offers practical exposure to these advanced frameworks.

MLOps for Generative Models

Traditional MLOps pipelines are evolving to support the unique requirements of generative AI, including:

Continuous Fine-Tuning: Updating models based on new data or feedback without full retraining, using techniques like incremental and transfer learning.

Prompt Engineering Lifecycle: Versioning and testing prompts as critical components of model performance, including methodologies for prompt optimization and impact evaluation.

Monitoring Generation Quality: Detecting hallucinations, bias, and drift in outputs, and implementing quality control measures.

Scalable Inference Infrastructure: Managing high-throughput, low-latency model serving with cost efficiency, leveraging cloud and edge computing.

Leading platforms such as MLflow, Kubeflow, and Amazon SageMaker are integrating MLOps for generative AI to streamline deployment and monitoring. Understanding MLOps for generative AI is now a foundational skill for teams building scalable agentic systems.

Cloud-Native and Edge Deployment

Agentic AI deployments increasingly leverage cloud-native architectures for scalability and resilience, using Kubernetes and serverless functions to manage agent workloads. Edge deployments are emerging for latency-sensitive applications like autonomous vehicles and IoT devices, where agents operate closer to data sources. This approach ensures real-time processing and reduces reliance on centralized infrastructure, topics often covered in advanced Generative AI and Agentic AI course curricula.

Advanced Tactics for Scalable, Reliable AI Systems

Modular Agent Design

Breaking down agent capabilities into modular, reusable components allows teams to iterate rapidly and isolate failures. Modular design supports parallel development and easier integration of new skills or data sources, facilitating continuous improvement and reducing system update complexity.

Robust Error Handling and Recovery

Agentic systems must anticipate and gracefully handle failures in external APIs, data inconsistencies, or unexpected inputs. Implementing fallback mechanisms, retries, and human-in-the-loop escalation ensures uninterrupted service and trustworthiness.

Data and Model Governance

Given the autonomy of agentic systems, governance frameworks are critical to manage data privacy, model biases, and compliance with regulations such as GDPR and HIPAA. Transparent logging and explainability tools help maintain accountability. This includes ensuring that data collection and processing align with ethical standards and legal requirements, a topic emphasized in MLOps for generative AI best practices.

Performance Optimization

Balancing model size, latency, and cost is vital. Techniques such as model distillation, quantization, and adaptive inference routing optimize resource use without sacrificing agent effectiveness. Leveraging hardware acceleration and optimizing software configurations further enhances performance.

Ethical Considerations and Governance

As Agentic AI systems become more autonomous, ethical considerations and governance practices become increasingly important. This includes ensuring transparency in decision-making, managing potential biases in AI outputs, and complying with regulatory frameworks. Recent developments in AI ethics frameworks emphasize the need for responsible AI deployment that prioritizes human values and safety. Professionals completing a Generative AI and Agentic AI course are well-positioned to implement these principles in practice.

The Role of Software Engineering Best Practices

The complexity of Agentic AI systems elevates the importance of mature software engineering principles:

Version Control for Code and Models: Ensures reproducibility and rollback capability.

Automated Testing: Unit, integration, and end-to-end tests validate agent logic and interactions.

Continuous Integration/Continuous Deployment (CI/CD): Automates safe and frequent updates.

Security by Design: Protects sensitive data and defends against adversarial attacks.

Documentation and Observability: Facilitates collaboration and troubleshooting across teams.

Embedding these practices into AI development pipelines is essential for operational excellence and long-term sustainability. Training in MLOps for generative AI equips teams with the skills to maintain these standards at scale.

Cross-Functional Collaboration for AI Success

Agentic AI projects succeed when data scientists, software engineers, product managers, and business stakeholders collaborate closely. This alignment ensures:

Clear definition of agent goals and KPIs.

Shared understanding of technical constraints and ethical considerations.

Coordinated deployment and change management.

Continuous feedback loops for iterative improvement.

Cross-functional teams foster innovation and reduce risks associated with misaligned expectations or siloed workflows. Those enrolled in an Agentic AI course in Mumbai with placements often experience this collaborative environment firsthand.

Measuring Success: Analytics and Monitoring

Effective monitoring of Agentic AI deployments includes:

Operational Metrics: Latency, uptime, throughput.

Performance Metrics: Accuracy, relevance, user satisfaction.

Behavioral Analytics: Agent decision paths, error rates, escalation frequency.

Business Outcomes: Cost savings, revenue impact, process efficiency.

Combining real-time dashboards with anomaly detection and alerting enables proactive management and continuous optimization of agentic systems. Mastering these analytics is a core outcome for participants in a Generative AI and Agentic AI course.

Case Study: Autonomous Supply Chain Optimization at DHL

DHL, a global logistics leader, exemplifies successful scaling of Agentic AI in 2025. Facing challenges of complex inventory management, fluctuating demand, and delivery delays, DHL deployed an autonomous supply chain agent powered by generative AI and real-time data orchestration.

The Journey

DHL’s agentic system integrates:

LLM-based demand forecasting models.

Autonomous routing agents coordinating with IoT sensors on shipments.

Dynamic inventory rebalancing modules adapting to disruptions.

The deployment involved iterative prototyping, cross-team collaboration, and rigorous MLOps for generative AI practices to ensure reliability and compliance across global operations.

Technical Challenges

Handling noisy sensor data and incomplete information.

Ensuring real-time decision-making under tight latency constraints.

Managing multi-regional regulatory compliance and data sovereignty.

Integrating legacy IT systems with new AI workflows.

Business Outcomes

20% reduction in delivery delays.

15% decrease in inventory holding costs.

Enhanced customer satisfaction through proactive communication.

Scalable platform enabling rapid rollout across regions.

DHL’s success highlights how agentic AI can transform complex, dynamic environments by combining autonomy with robust engineering and collaborative execution. Professionals trained through an Agentic AI course in Mumbai with placements are well-prepared to tackle similar challenges.

Additional Case Study: Personalized Healthcare with Agentic AI

In healthcare, Agentic AI is revolutionizing patient care by providing personalized treatment plans and improving patient outcomes. For instance, a healthcare provider might deploy an agentic system to analyze patient data, adapt treatment strategies based on real-time health conditions, and optimize resource allocation in hospitals. This involves integrating AI with electronic health records, wearable devices, and clinical decision support systems to enhance care quality and efficiency.

Technical Implementation

Data Integration: Combining data from various sources to create comprehensive patient profiles.

AI-Driven Decision Support: Using machine learning models to predict patient outcomes and suggest personalized interventions.

Real-Time Monitoring: Continuously monitoring patient health and adjusting treatment plans accordingly.

Business Outcomes

Improved patient satisfaction through personalized care.

Enhanced resource allocation and operational efficiency.

Better clinical outcomes due to real-time decision-making.

This case study demonstrates how Agentic AI can improve healthcare outcomes by leveraging autonomy and adaptability in dynamic environments. A Generative AI and Agentic AI course provides the multidisciplinary knowledge required for such implementations.

Actionable Tips and Lessons Learned

Start small but think big: Pilot agentic AI on well-defined use cases to gather data and refine models before scaling.

Invest in MLOps tailored for generative AI: Automate continuous training, testing, and monitoring to ensure robust deployments.

Design agents modularly: Facilitate updates and integration of new capabilities.

Prioritize explainability and governance: Build trust with stakeholders and comply with regulations.

Foster cross-functional teams: Align technical and business goals early and often.

Monitor holistically: Combine operational, performance, and business metrics for comprehensive insights.

Plan for human-in-the-loop: Use human oversight strategically to handle edge cases and improve agent learning.

For those considering a career shift, an Agentic AI course in Mumbai with placements offers a structured pathway to acquire these skills and gain practical experience.

Conclusion

Scaling Agentic AI in 2025 is both a technical and organizational challenge demanding advanced frameworks, rigorous engineering discipline, and tight collaboration across teams. The evolution from narrow AI to autonomous, adaptive agents unlocks unprecedented efficiencies and capabilities across industries. Real-world deployments like DHL’s autonomous supply chain agent demonstrate the transformative potential when cutting-edge AI meets sound software engineering and business acumen.

For AI practitioners and technology leaders, success lies in embracing modular architectures, investing in MLOps for generative AI, prioritizing governance, and fostering cross-functional collaboration. Monitoring and continuous improvement complete the cycle, ensuring agentic systems deliver measurable business value while maintaining reliability and compliance.

Agentic AI is not just an evolution of technology but a revolution in how businesses operate and innovate. The time to build scalable, trustworthy agentic AI systems is now. Whether you are looking to upskill or transition into this field, a Generative AI and Agentic AI course can provide the knowledge, tools, and industry connections to accelerate your journey.

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codezup · 20 days ago

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Building Scalable Machine Learning Pipelines with Kubeflow & Kubernetes

Building Scalable Machine Learning Pipelines with Kubeflow and Kubernetes 1. Introduction 1.1 Overview Building scalable machine learning (ML) pipelines is crucial for modern data-driven applications. These pipelines automate the end-to-end ML workflow, from data preparation to model deployment, enabling efficient and reproducible processes. Kubeflow and Kubernetes provide a robust framework…

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callofdutymobileindia · 25 days ago

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Career Opportunities After Completing a Machine Learning Course in Pune

As industries increasingly adopt data-driven strategies, the demand for machine learning professionals is skyrocketing. Pune, a thriving tech and education hub in India, is rapidly becoming a favored destination for aspiring machine learning experts. If you're considering a Machine Learning Course in Pune, you're not just investing in education—you're setting the stage for a dynamic and rewarding career.

In this article, we explore the wide array of career opportunities that await you after completing a machine learning course in Pune, the industries actively hiring, the roles you can pursue, and why Pune is an ideal place to start this journey.

Why Choose Pune for a Machine Learning Course?

Pune is home to some of India’s leading IT companies, start-ups, and research institutions. Its well-established academic infrastructure and thriving tech community make it an attractive city for learning emerging technologies like machine learning (ML), artificial intelligence (AI), and data science.

Here are a few reasons why enrolling in a Machine Learning Course in Pune is a smart decision:

Tech Ecosystem: Pune hosts major IT parks like Hinjewadi and Magarpatta, offering direct access to tech companies hiring ML talent.

Affordable Education: Compared to metros like Bengaluru and Mumbai, Pune offers high-quality courses at relatively lower costs.

Startup Culture: The city’s booming startup scene creates abundant opportunities for machine learning professionals to work on real-world projects.

Career Opportunities After a Machine Learning Course in Pune

After completing a machine learning course in Pune, you’ll be equipped with in-demand skills that open the door to several lucrative career paths. Let’s dive into the key roles available:

1. Machine Learning Engineer

A machine learning engineer designs and implements ML models that automate decision-making. You’ll work with algorithms, data pipelines, and model evaluation techniques.

Skills Required:

Python, R, or Java

Scikit-learn, TensorFlow, PyTorch

Data pre-processing and model tuning

Average Salary in Pune: ₹8–15 LPA

2. Data Scientist

Data scientists use machine learning, statistical models, and domain expertise to extract insights and predict future outcomes from large datasets.

Skills Required:

Data visualization (Tableau, Power BI)

SQL, Python, Pandas, NumPy

Deep learning and NLP

Average Salary in Pune: ₹10–20 LPA

3. AI Researcher

AI researchers focus on developing new algorithms and contributing to the evolution of artificial intelligence. This role is more research-oriented and often found in R&D centers or academia.

Skills Required:

Advanced mathematics and statistics

Reinforcement learning, neural networks

Research publication and academic writing

Average Salary in Pune: ₹12–25 LPA

4. Business Intelligence Analyst

A BI analyst leverages machine learning to optimize business strategies. This role is ideal if you enjoy both data and business logic.

Skills Required:

Excel, SQL, business dashboards

Predictive modeling

Problem-solving and communication

Average Salary in Pune: ₹7–12 LPA

5. Data Analyst with ML Specialization

While traditional data analysts focus on reports and visualization, those with machine learning skills can build predictive models and automated systems for decision-making.

Skills Required:

Data mining

Regression analysis

Python/R for ML

Average Salary in Pune: ₹6–10 LPA

6. ML DevOps Engineer

This hybrid role combines machine learning with DevOps. You’ll ensure smooth deployment and scaling of ML models in production environments.

Skills Required:

MLOps tools (MLflow, Kubeflow)

CI/CD pipelines

Docker, Kubernetes

Average Salary in Pune: ₹10–18 LPA

Industries Hiring Machine Learning Professionals in Pune

Pune’s diverse economy ensures that machine learning roles aren’t restricted to just IT companies. Here's a look at the top industries recruiting ML talent:

● Information Technology

IT giants like Infosys, Cognizant, and Wipro have large campuses in Pune and actively hire machine learning engineers for enterprise-level projects.

● Automotive and Manufacturing

With companies like Tata Motors and Bajaj Auto headquartered in Pune, there’s increasing use of ML for predictive maintenance, quality control, and supply chain optimization.

● Finance and Fintech

Fintech startups and banks in Pune use machine learning for fraud detection, credit scoring, and algorithmic trading.

● Healthcare and Biotech

Machine learning is transforming healthcare in Pune through medical imaging, diagnostics, and patient data analytics.

● E-commerce and Retail

E-commerce players rely heavily on ML for recommendation systems, customer segmentation, and inventory optimization.

Freelancing and Remote Work Opportunities

Another major advantage of completing a Machine Learning Course in Pune is access to remote and freelance opportunities. As a certified ML professional, you can:

Work remotely for global companies

Offer consultancy services to startups

Freelance on platforms like Upwork, Toptal, and Freelancer

Build and monetize ML models or data products

This flexibility is especially beneficial if you're looking to build a location-independent career or earn side income while studying or working full-time.

Skill Enhancement After Your ML Course

To stand out in Pune’s competitive job market, consider supplementing your ML course with the following skills:

Cloud Platforms: Learn AWS, GCP, or Azure for deploying ML models.

Big Data Tools: Familiarity with Hadoop, Spark, or Kafka is a plus.

Domain Knowledge: Understand the domain you want to work in—be it finance, healthcare, or retail.

Soft Skills: Communication, collaboration, and problem-solving are essential in real-world projects.

Internships and Placement Assistance

Leading machine learning courses in Pune often come with internship opportunities and placement support. These programs:

Help you gain hands-on industry experience

Improve your resume with real-world projects

Offer networking opportunities with hiring partners

Choosing a course with robust career services can significantly boost your chances of landing your first ML job.

Final Thoughts

Completing anArtificial Intelligence Classroom Course in Pune can be your gateway to a high-growth, high-paying tech career. Pune’s thriving IT ecosystem, startup culture, and educational excellence make it a prime destination for anyone looking to dive into machine learning. Whether you aim to become a data scientist, ML engineer, or AI researcher, the career opportunities are both diverse and rewarding.

As the world increasingly embraces AI and automation, machine learning skills will only grow in demand. Equip yourself with the right knowledge, gain practical experience, and stay updated with industry trends—and your machine learning journey from Pune will set you up for long-term success.

#Best Data Science Courses in Mumbai #Artificial Intelligence Course in Mumbai #Data Scientist Course in Mumbai #Machine Learning Course in Mumbai

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coredgeblogs · 29 days ago

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From Code to Production: Streamlining the ML Lifecycle with Kubernetes and Kubeflow

In today’s AI-driven landscape, organizations are increasingly looking to scale their machine learning (ML) initiatives from isolated experiments to production-grade deployments. However, operationalizing ML is not trivial—it involves a complex set of challenges including infrastructure management, workflow automation, reproducibility, and deployment governance.

To address these, industry leaders are turning to Kubernetes and Kubeflow—tools that bring DevOps best practices to the ML lifecycle, enabling scalable, reliable, and maintainable ML workflows across teams and environments.

The Complexity of Operationalizing Machine Learning

While data scientists often begin with model development in local environments or notebooks, this initial experimentation phase represents only a fraction of the full ML lifecycle. Moving from prototype to production requires:

Coordinating multi-step workflows (e.g., preprocessing, training, validation, deployment)

Managing compute-intensive tasks and scaling across GPUs or distributed environments

Ensuring reproducibility across versions, datasets, and model iterations

Enabling continuous integration and delivery (CI/CD) for ML pipelines

Monitoring model performance and retraining when necessary

Without the right infrastructure, these steps become manual, error-prone, and difficult to maintain at scale.

Kubernetes: The Infrastructure Backbone

Kubernetes has emerged as the de facto standard for container orchestration and infrastructure automation. Its relevance in ML stems from its ability to:

Dynamically allocate compute resources based on workload requirements

Standardize deployment environments across cloud and on-premise infrastructure

Provide high availability, fault tolerance, and scalability for training and serving

Enable microservices-based architecture for modular, maintainable ML pipelines

By containerizing ML workloads and running them on Kubernetes, teams gain consistency, flexibility, and control—essential attributes for production-grade ML.

Kubeflow: Machine Learning at Scale

Kubeflow, built on Kubernetes, is a dedicated platform for managing the entire ML lifecycle. It abstracts the complexities of infrastructure, allowing teams to focus on modeling and experimentation while automating the rest. Key features include:

Kubeflow Pipelines: Define and orchestrate repeatable, modular ML workflows

Training Operators: Support for distributed training frameworks (e.g., TensorFlow, PyTorch)

Katib: Automated hyperparameter tuning at scale

KFServing (KServe): Scalable, serverless model serving

Centralized Notebook Environments: Managed Jupyter notebooks running securely within the cluster

Kubeflow enables organizations to enforce consistency, governance, and observability across all stages of ML development and deployment.

Business Impact and Technical Advantages

Implementing Kubernetes and Kubeflow in ML operations delivers tangible benefits:

Increased Operational Efficiency: Reduced manual effort through automation and CI/CD for ML

Scalability and Flexibility: Easily scale workloads to meet demand, across any cloud or hybrid environment

Improved Reproducibility and Compliance: Version control for datasets, code, and model artifacts

Accelerated Time-to-Value: Faster transition from model experimentation to business impact

These platforms also support better collaboration between data science, engineering, and DevOps teams, driving organizational alignment and reducing friction in model deployment processes.

Conclusion

As enterprises continue to invest in AI/ML, the need for robust, scalable, and repeatable operational practices has never been greater. Kubernetes and Kubeflow provide a powerful foundation to manage the end-to-end ML lifecycle—from code to production.

Organizations that adopt these tools are better positioned to drive innovation, reduce operational overhead, and realize the full potential of their machine learning initiatives.

#artificial intelligence #sovereign ai #coding #devlog #entrepreneur #linux #economy #html #indiedev

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hawkstack · 1 month ago

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Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI268)

As artificial intelligence (AI) and machine learning (ML) become central to enterprise innovation, organizations are seeking platforms and tools that streamline the development, deployment, and management of intelligent applications. Red Hat OpenShift AI (formerly known as Red Hat OpenShift Data Science) provides a robust, scalable, and secure foundation for building intelligent applications — and the AI268 course is your gateway to mastering this powerful ecosystem.

In this blog post, we'll explore what the AI268 – Developing and Deploying AI/ML Applications on Red Hat OpenShift AI course offers, who it’s for, and why it’s crucial for modern data scientists, ML engineers, and developers working in hybrid cloud environments.

What is Red Hat OpenShift AI?

Red Hat OpenShift AI is an enterprise-ready platform that brings together tools for the entire AI/ML lifecycle — from model development to training, deployment, monitoring, and retraining. Built on OpenShift, Red Hat’s industry-leading Kubernetes platform, OpenShift AI integrates open source AI frameworks, Jupyter notebooks, model serving frameworks, and MLOps tools like KServe and Kubeflow Pipelines.

It’s designed to:

Accelerate AI/ML development with pre-integrated tools.

Enable collaboration between data scientists and developers.

Simplify deployment of models to production environments.

Ensure compliance, scalability, and lifecycle management.

About the AI268 Course

Course Name: Developing and Deploying AI/ML Applications on Red Hat OpenShift AI Course Code: AI268 Delivery: Classroom, Virtual, or Self-paced (via Red Hat Learning Subscription) Duration: 4 days (may vary based on delivery mode) Skill Level: Intermediate to Advanced

What You’ll Learn

AI268 is a hands-on course that covers the entire journey of AI/ML application development within the OpenShift AI platform. Participants will learn how to:

Use JupyterLab for exploratory data analysis and model development.

Leverage OpenShift AI components like Pipelines, Workbenches, and Model Serving.

Train, deploy, and monitor models in a containerized, Kubernetes-native environment.

Implement MLOps practices for versioning, automation, and reproducibility.

Work collaboratively across roles — from data science to operations.

Key Topics Covered

Introduction to OpenShift AI and its architecture

Building models using Jupyter notebooks and popular ML libraries (e.g., scikit-learn, PyTorch)

Automating training workflows with Kubeflow Pipelines and OpenShift Pipelines

Model serving using KServe

Version control and experiment tracking with MLflow

Securing and scaling AI/ML workloads in hybrid cloud environments

Who Should Take This Course?

This course is ideal for:

Data Scientists looking to transition from local development to scalable, production-grade platforms.

Machine Learning Engineers who want to operationalize ML pipelines.

DevOps and Platform Engineers supporting AI workloads on Kubernetes.

IT Architects interested in building secure and scalable AI/ML platforms.

Prerequisites include a solid understanding of data science fundamentals, Python, and container concepts. Familiarity with Kubernetes or OpenShift is recommended but not mandatory.

Why Choose Red Hat OpenShift AI for Your AI/ML Journey?

Red Hat OpenShift AI enables teams to bring AI/ML applications from research to production with consistency and reliability. Whether you're building predictive analytics tools, real-time inference engines, or large-scale ML platforms, OpenShift AI gives you the tools to innovate without compromising security or compliance.

AI268 equips you with the skills to thrive in this environment — by aligning data science workflows with enterprise IT standards.

Take the Next Step

Ready to accelerate your career in AI/ML and bring real business value to your organization? The AI268 course will help you:

✅ Develop AI/ML applications faster ✅ Deploy models at scale with confidence ✅ Implement MLOps best practices in OpenShift ✅ Prepare for Red Hat certification paths in AI/ML

Explore Red Hat’s Learning Subscription to access this course and others, or reach out to us at HawkStack Technologies — a Red Hat Training Partner — to enroll in the next batch.

🚀 Empower Your AI/ML Teams with Red Hat OpenShift AI

Whether you're starting your AI/ML journey or scaling up existing models, AI268 helps bridge the gap between innovation and implementation. Let Red Hat OpenShift AI be your platform for intelligent enterprise applications.

For more details www.hawkstack.com

#hawkstack #hawkstack technologies #kubernetes #redhat #ansible #automation #openshift

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seodigital7 · 1 month ago

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Machine Learning Infrastructure: The Foundation of Scalable AI Solutions

Introduction: Why Machine Learning Infrastructure Matters

In today's digital-first world, the adoption of artificial intelligence (AI) and machine learning (ML) is revolutionizing every industry—from healthcare and finance to e-commerce and entertainment. However, while many organizations aim to leverage ML for automation and insights, few realize that success depends not just on algorithms, but also on a well-structured machine learning infrastructure.

Machine learning infrastructure provides the backbone needed to deploy, monitor, scale, and maintain ML models effectively. Without it, even the most promising ML solutions fail to meet their potential.

In this comprehensive guide from diglip7.com, we’ll explore what machine learning infrastructure is, why it’s crucial, and how businesses can build and manage it effectively.

What is Machine Learning Infrastructure?

Machine learning infrastructure refers to the full stack of tools, platforms, and systems that support the development, training, deployment, and monitoring of ML models. This includes:

Data storage systems

Compute resources (CPU, GPU, TPU)

Model training and validation environments

Monitoring and orchestration tools

Version control for code and models

Together, these components form the ecosystem where machine learning workflows operate efficiently and reliably.

Key Components of Machine Learning Infrastructure

To build robust ML pipelines, several foundational elements must be in place:

1. Data Infrastructure

Data is the fuel of machine learning. Key tools and technologies include:

Data Lakes & Warehouses: Store structured and unstructured data (e.g., AWS S3, Google BigQuery).

ETL Pipelines: Extract, transform, and load raw data for modeling (e.g., Apache Airflow, dbt).

Data Labeling Tools: For supervised learning (e.g., Labelbox, Amazon SageMaker Ground Truth).

2. Compute Resources

Training ML models requires high-performance computing. Options include:

On-Premise Clusters: Cost-effective for large enterprises.

Cloud Compute: Scalable resources like AWS EC2, Google Cloud AI Platform, or Azure ML.

GPUs/TPUs: Essential for deep learning and neural networks.

3. Model Training Platforms

These platforms simplify experimentation and hyperparameter tuning:

TensorFlow, PyTorch, Scikit-learn: Popular ML libraries.

MLflow: Experiment tracking and model lifecycle management.

KubeFlow: ML workflow orchestration on Kubernetes.

4. Deployment Infrastructure

Once trained, models must be deployed in real-world environments:

Containers & Microservices: Docker, Kubernetes, and serverless functions.

Model Serving Platforms: TensorFlow Serving, TorchServe, or custom REST APIs.

CI/CD Pipelines: Automate testing, integration, and deployment of ML models.

5. Monitoring & Observability

Key to ensure ongoing model performance:

Drift Detection: Spot when model predictions diverge from expected outputs.

Performance Monitoring: Track latency, accuracy, and throughput.

Logging & Alerts: Tools like Prometheus, Grafana, or Seldon Core.

Benefits of Investing in Machine Learning Infrastructure

Here’s why having a strong machine learning infrastructure matters:

Scalability: Run models on large datasets and serve thousands of requests per second.

Reproducibility: Re-run experiments with the same configuration.

Speed: Accelerate development cycles with automation and reusable pipelines.

Collaboration: Enable data scientists, ML engineers, and DevOps to work in sync.

Compliance: Keep data and models auditable and secure for regulations like GDPR or HIPAA.

Real-World Applications of Machine Learning Infrastructure

Let’s look at how industry leaders use ML infrastructure to power their services:

Netflix: Uses a robust ML pipeline to personalize content and optimize streaming.

Amazon: Trains recommendation models using massive data pipelines and custom ML platforms.

Tesla: Collects real-time driving data from vehicles and retrains autonomous driving models.

Spotify: Relies on cloud-based infrastructure for playlist generation and music discovery.

Challenges in Building ML Infrastructure

Despite its importance, developing ML infrastructure has its hurdles:

High Costs: GPU servers and cloud compute aren't cheap.

Complex Tooling: Choosing the right combination of tools can be overwhelming.

Maintenance Overhead: Regular updates, monitoring, and security patching are required.

Talent Shortage: Skilled ML engineers and MLOps professionals are in short supply.

How to Build Machine Learning Infrastructure: A Step-by-Step Guide

Here’s a simplified roadmap for setting up scalable ML infrastructure:

Step 1: Define Use Cases

Know what problem you're solving. Fraud detection? Product recommendations? Forecasting?

Step 2: Collect & Store Data

Use data lakes, warehouses, or relational databases. Ensure it’s clean, labeled, and secure.

Step 3: Choose ML Tools

Select frameworks (e.g., TensorFlow, PyTorch), orchestration tools, and compute environments.

Step 4: Set Up Compute Environment

Use cloud-based Jupyter notebooks, Colab, or on-premise GPUs for training.

Step 5: Build CI/CD Pipelines

Automate model testing and deployment with Git, Jenkins, or MLflow.

Step 6: Monitor Performance

Track accuracy, latency, and data drift. Set alerts for anomalies.

Step 7: Iterate & Improve

Collect feedback, retrain models, and scale solutions based on business needs.

Machine Learning Infrastructure Providers & Tools

Below are some popular platforms that help streamline ML infrastructure: Tool/PlatformPurposeExampleAmazon SageMakerFull ML development environmentEnd-to-end ML pipelineGoogle Vertex AICloud ML serviceTraining, deploying, managing ML modelsDatabricksBig data + MLCollaborative notebooksKubeFlowKubernetes-based ML workflowsModel orchestrationMLflowModel lifecycle trackingExperiments, models, metricsWeights & BiasesExperiment trackingVisualization and monitoring

Expert Review

Reviewed by: Rajeev Kapoor, Senior ML Engineer at DataStack AI

"Machine learning infrastructure is no longer a luxury; it's a necessity for scalable AI deployments. Companies that invest early in robust, cloud-native ML infrastructure are far more likely to deliver consistent, accurate, and responsible AI solutions."

Frequently Asked Questions (FAQs)

Q1: What is the difference between ML infrastructure and traditional IT infrastructure?

Answer: Traditional IT supports business applications, while ML infrastructure is designed for data processing, model training, and deployment at scale. It often includes specialized hardware (e.g., GPUs) and tools for data science workflows.

Q2: Can small businesses benefit from ML infrastructure?

Answer: Yes, with the rise of cloud platforms like AWS SageMaker and Google Vertex AI, even startups can leverage scalable machine learning infrastructure without heavy upfront investment.

Q3: Is Kubernetes necessary for ML infrastructure?

Answer: While not mandatory, Kubernetes helps orchestrate containerized workloads and is widely adopted for scalable ML infrastructure, especially in production environments.

Q4: What skills are needed to manage ML infrastructure?

Answer: Familiarity with Python, cloud computing, Docker/Kubernetes, CI/CD, and ML frameworks like TensorFlow or PyTorch is essential.

Q5: How often should ML models be retrained?

Answer: It depends on data volatility. In dynamic environments (e.g., fraud detection), retraining may occur weekly or daily. In stable domains, monthly or quarterly retraining suffices.

Final Thoughts

Machine learning infrastructure isn’t just about stacking technologies—it's about creating an agile, scalable, and collaborative environment that empowers data scientists and engineers to build models with real-world impact. Whether you're a startup or an enterprise, investing in the right infrastructure will directly influence the success of your AI initiatives.

By building and maintaining a robust ML infrastructure, you ensure that your models perform optimally, adapt to new data, and generate consistent business value.

For more insights and updates on AI, ML, and digital innovation, visit diglip7.com.

#blog posting #digital marketing #backlink #offline seo marketing

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sid099 · 2 months ago

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Step-by-Step Guide to Hiring an MLOps Engineer

: Steps to Hire an MLOps Engineer Make the role clear.

Decide your needs: model deployment, CI/CD for ML, monitoring, cloud infrastructure, etc.

2. Choose the level (junior, mid, senior) depending on how advanced the project is.

Create a concise job description.

Include responsibilities like:

2. ML workflow automation (CI/CD)

3. Model lifecycle management (training to deployment)

4. Model performance tracking

5. Utilizing Docker, Kubernetes, Airflow, MLflow, etc.

: Emphasize necessary experience with ML libraries (TensorFlow, PyTorch), cloud platforms (AWS, GCP, Azure), and DevOps tools.

: Source Candidates

Utilize dedicated platforms: LinkedIn, Stack Overflow, GitHub, and AI/ML forums (e.g., MLOps Community, Weights & Biases forums).

Use freelancers or agencies on a temporary or project-by-project basis.

1. Screen Resumes for Technical Skills

2. Look for experience in:

3. Building responsive machine learning pipelines

4 .Employing in a cloud-based environment

5. Managing manufacturing ML systems

: Technical Interview & Assessment

Add coding and system design rounds.

Check understanding of:

1.CI/CD for ML

2. Container management.

3. Monitoring & logging (e.g., Prometheus, Grafana)

4. Tracking experiments

Optional: hands-on exercise or take-home assignment (e.g., build a simple training-to-deployment pipeline).

1. Evaluate Soft Skills & Culture Fit

2. Collaboration with data scientists, software engineers, and product managers is necessary.

3. Assess communication, documentation style, and collaboration.

4. Make an Offer & Onboard

5. Offer thorough onboarding instructions.

6. Begin with a real project to see the impact soon.

Mlops engineer

???? Most Important Points to Remember MLOps ≠ DevOps: MLOps introduces additional complexity — model versioning, drift, data pipelines.

Infrastructure experience is a must: Hire individuals who have experience with cloud, containers, and orchestration tools.

Cross-function thinking: This is where MLOps intersect IT, software development, and machine learning—clear communications are crucial.

Knowledge tools: MLflow, Kubeflow, Airflow, DVC, Terraform, Docker, and Kubernetes are typical.

Security and scalability: Consider if the candidate has developed secure and scalable machine learning systems.

Model monitoring and feedback loops: Make sure they know how to check and keep the model’s performance good over time.

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digitalmore · 2 months ago

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#IFTTT #Digital More

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differenttimemachinecrusade · 3 months ago

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Cloud Native Storage Market Insights: Industry Share, Trends & Future Outlook 2032

TheCloud Native Storage Market Size was valued at USD 16.19 Billion in 2023 and is expected to reach USD 100.09 Billion by 2032 and grow at a CAGR of 22.5% over the forecast period 2024-2032

The cloud native storage market is experiencing rapid growth as enterprises shift towards scalable, flexible, and cost-effective storage solutions. The increasing adoption of cloud computing and containerization is driving demand for advanced storage technologies.

The cloud native storage market continues to expand as businesses seek high-performance, secure, and automated data storage solutions. With the rise of hybrid cloud, Kubernetes, and microservices architectures, organizations are investing in cloud native storage to enhance agility and efficiency in data management.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/3454

Market Keyplayers:

Microsoft (Azure Blob Storage, Azure Kubernetes Service (AKS))

IBM, (IBM Cloud Object Storage, IBM Spectrum Scale)

AWS (Amazon S3, Amazon EBS (Elastic Block Store))

Google (Google Cloud Storage, Google Kubernetes Engine (GKE))

Alibaba Cloud (Alibaba Object Storage Service (OSS), Alibaba Cloud Container Service for Kubernetes)

VMWare (VMware vSAN, VMware Tanzu Kubernetes Grid)

Huawei (Huawei FusionStorage, Huawei Cloud Object Storage Service)

Citrix (Citrix Hypervisor, Citrix ShareFile)

Tencent Cloud (Tencent Cloud Object Storage (COS), Tencent Kubernetes Engine)

Scality (Scality RING, Scality ARTESCA)

Splunk (Splunk SmartStore, Splunk Enterprise on Kubernetes)

Linbit (LINSTOR, DRBD (Distributed Replicated Block Device))

Rackspace (Rackspace Object Storage, Rackspace Managed Kubernetes)

Robin.Io (Robin Cloud Native Storage, Robin Multi-Cluster Automation)

MayaData (OpenEBS, Data Management Platform (DMP))

Diamanti (Diamanti Ultima, Diamanti Spektra)

Minio (MinIO Object Storage, MinIO Kubernetes Operator)

Rook (Rook Ceph, Rook EdgeFS)

Ondat (Ondat Persistent Volumes, Ondat Data Mesh)

Ionir (Ionir Data Services Platform, Ionir Continuous Data Mobility)

Trilio (TrilioVault for Kubernetes, TrilioVault for OpenStack)

Upcloud (UpCloud Object Storage, UpCloud Managed Databases)

Arrikto (Kubeflow Enterprise, Rok (Data Management for Kubernetes)

Market Size, Share, and Scope

The market is witnessing significant expansion across industries such as IT, BFSI, healthcare, retail, and manufacturing.

Hybrid and multi-cloud storage solutions are gaining traction due to their flexibility and cost-effectiveness.

Enterprises are increasingly adopting object storage, file storage, and block storage tailored for cloud native environments.

Key Market Trends Driving Growth

Rise in Cloud Adoption: Organizations are shifting workloads to public, private, and hybrid cloud environments, fueling demand for cloud native storage.

Growing Adoption of Kubernetes: Kubernetes-based storage solutions are becoming essential for managing containerized applications efficiently.

Increased Data Security and Compliance Needs: Businesses are investing in encrypted, resilient, and compliant storage solutions to meet global data protection regulations.

Advancements in AI and Automation: AI-driven storage management and self-healing storage systems are revolutionizing data handling.

Surge in Edge Computing: Cloud native storage is expanding to edge locations, enabling real-time data processing and low-latency operations.

Integration with DevOps and CI/CD Pipelines: Developers and IT teams are leveraging cloud storage automation for seamless software deployment.

Hybrid and Multi-Cloud Strategies: Enterprises are implementing multi-cloud storage architectures to optimize performance and costs.

Increased Use of Object Storage: The scalability and efficiency of object storage are driving its adoption in cloud native environments.

Serverless and API-Driven Storage Solutions: The rise of serverless computing is pushing demand for API-based cloud storage models.

Sustainability and Green Cloud Initiatives: Energy-efficient storage solutions are becoming a key focus for cloud providers and enterprises.

Enquiry of This Report: https://www.snsinsider.com/enquiry/3454

Market Segmentation:

By Component

Solution

Object Storage

Block Storage

File Storage

Container Storage

Others

Services

System Integration & Deployment

Training & Consulting

Support & Maintenance

By Deployment

Private Cloud

Public Cloud

By Enterprise Size

SMEs

Large Enterprises

By End Use

BFSI

Telecom & IT

Healthcare

Retail & Consumer Goods

Manufacturing

Government

Energy & Utilities

Media & Entertainment

Others

Market Growth Analysis

Factors Driving Market Expansion

The growing need for cost-effective and scalable data storage solutions

Adoption of cloud-first strategies by enterprises and governments

Rising investments in data center modernization and digital transformation

Advancements in 5G, IoT, and AI-driven analytics

Industry Forecast 2032: Size, Share & Growth Analysis

The cloud native storage market is projected to grow significantly over the next decade, driven by advancements in distributed storage architectures, AI-enhanced storage management, and increasing enterprise digitalization.

North America leads the market, followed by Europe and Asia-Pacific, with China and India emerging as key growth hubs.

The demand for software-defined storage (SDS), container-native storage, and data resiliency solutions will drive innovation and competition in the market.

Future Prospects and Opportunities

1. Expansion in Emerging Markets

Developing economies are expected to witness increased investment in cloud infrastructure and storage solutions.

2. AI and Machine Learning for Intelligent Storage

AI-powered storage analytics will enhance real-time data optimization and predictive storage management.

3. Blockchain for Secure Cloud Storage

Blockchain-based decentralized storage models will offer improved data security, integrity, and transparency.

4. Hyperconverged Infrastructure (HCI) Growth

Enterprises are adopting HCI solutions that integrate storage, networking, and compute resources.

5. Data Sovereignty and Compliance-Driven Solutions

The demand for region-specific, compliant storage solutions will drive innovation in data governance technologies.

Access Complete Report: https://www.snsinsider.com/reports/cloud-native-storage-market-3454

Conclusion

The cloud native storage market is poised for exponential growth, fueled by technological innovations, security enhancements, and enterprise digital transformation. As businesses embrace cloud, AI, and hybrid storage strategies, the future of cloud native storage will be defined by scalability, automation, and efficiency.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

#cloud native storage market #cloud native storage market Scope #cloud native storage market Size #cloud native storage market Analysis #cloud native storage market Trends

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govindhtech · 7 months ago

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What Is AWS EKS? Use EKS To Simplify Kubernetes On AWS

What Is AWS EKS?

AWS EKS, a managed service, eliminates the need to install, administer, and maintain your own Kubernetes control plane on Amazon Web Services (AWS). Kubernetes simplifies containerized app scaling, deployment, and management.

How it Works?

AWS Elastic Kubernetes Service (Amazon EKS) is a managed Kubernetes solution for on-premises data centers and the AWS cloud. The Kubernetes control plane nodes in the cloud that are in charge of scheduling containers, controlling application availability, storing cluster data, and other crucial functions are automatically managed in terms of scalability and availability by AWS EKS.

You can benefit from all of AWS infrastructure’s performance, scalability, dependability, and availability with Amazon EKS. You can also integrate AWS networking and security services. When deployed on-premises on AWS Outposts, virtual machines, or bare metal servers, EKS offers a reliable, fully supported Kubernetes solution with integrated tools.Image Credit To Amazon Web Services

AWS EKS advantages

Integration of AWS Services

Make use of the integrated AWS services, including EC2, VPC, IAM, EBS, and others.

Cost reductions with Kubernetes

Use automated Kubernetes application scalability and effective computing resource provisioning to cut expenses.

Security of automated Kubernetes control planes

By automatically applying security fixes to the control plane of your cluster, you can guarantee a more secure Kubernetes environment

Use cases

Implement in a variety of hybrid contexts

Run Kubernetes in your data centers and manage your Kubernetes clusters and apps in hybrid environments.

Workflows for model machine learning (ML)

Use the newest GPU-powered instances from Amazon Elastic Compute Cloud (EC2), such as Inferentia, to efficiently execute distributed training jobs. Kubeflow is used to deploy training and inferences.

Create and execute web apps

With innovative networking and security connections, develop applications that operate in a highly available configuration across many Availability Zones (AZs) and automatically scale up and down.

Amazon EKS Features

Running Kubernetes on AWS and on-premises is made simple with Amazon Elastic Kubernetes Service (AWS EKS), a managed Kubernetes solution. An open-source platform called Kubernetes makes it easier to scale, deploy, and maintain containerized apps. Existing apps that use upstream Kubernetes can be used with Amazon EKS as it is certified Kubernetes-conformant.

The Kubernetes control plane nodes that schedule containers, control application availability, store cluster data, and perform other crucial functions are automatically scaled and made available by Amazon EKS.

You may run your Kubernetes apps on AWS Fargate and Amazon Elastic Compute Cloud (Amazon EC2) using Amazon EKS. You can benefit from all of AWS infrastructure’s performance, scalability, dependability, and availability with Amazon EKS. It also integrates with AWS networking and security services, including AWS Virtual Private Cloud (VPC) support for pod networking, AWS Identity and Access Management (IAM) integration with role-based access control (RBAC), and application load balancers (ALBs) for load distribution.

Managed Kubernetes Clusters

Managed Control Plane

Across several AWS Availability Zones (AZs), AWS EKS offers a highly available and scalable Kubernetes control plane. The scalability and availability of Kubernetes API servers and the etcd persistence layer are automatically managed by Amazon EKS. To provide high availability, Amazon EKS distributes the Kubernetes control plane throughout three AZs. It also automatically identifies and swaps out sick control plane nodes.

Service Integrations

You may directly manage AWS services from within your Kubernetes environment with AWS Controllers for Kubernetes (ACK). Building scalable and highly available Kubernetes apps using AWS services is made easy with ACK.

Hosted Kubernetes Console

For Kubernetes clusters, EKS offers an integrated console. Kubernetes apps running on AWS EKS may be arranged, visualized, and troubleshooted in one location by cluster operators and application developers using EKS. All EKS clusters have automatic access to the EKS console, which is hosted by AWS.

EKS Add-Ons

Common operational software for expanding the operational capability of Kubernetes is EKS add-ons. The add-on software may be installed and updated via EKS. Choose whatever add-ons, such as Kubernetes tools for observability, networking, auto-scaling, and AWS service integrations, you want to run in an Amazon EKS cluster when you first launch it.

Managed Node Groups

With just one command, you can grow, terminate, update, and build nodes for your cluster using AWS EKS. To cut expenses, these nodes can also make use of Amazon EC2 Spot Instances. Updates and terminations smoothly deplete nodes to guarantee your apps stay accessible, while managed node groups operate Amazon EC2 instances utilizing the most recent EKS-optimized or customized Amazon Machine Images (AMIs) in your AWS account.

AWS EKS Connector

Any conformant Kubernetes cluster may be connected to AWS using AWS EKS, and it can be seen in the Amazon EKS dashboard. Any conformant Kubernetes cluster can be connected, including self-managed clusters on Amazon Elastic Compute Cloud (Amazon EC2), Amazon EKS Anywhere clusters operating on-premises, and other Kubernetes clusters operating outside of AWS. You can access all linked clusters and the Kubernetes resources running on them using the Amazon EKS console, regardless of where your cluster is located.