Course Description:

This intensive 4-Part course is designed for IT professionals, DevOps engineers, and AI practitioners who are keen to integrate AI applications with Kubernetes for scalable, robust, and efficient deployment. As the use of artificial intelligence (AI) continues to expand across industries, the need for managing AI workloads efficiently in production environments is critical. Kubernetes, as the leading container orchestration platform, offers an ideal framework to deploy, scale, and monitor AI applications.

Through a combination of lectures, hands-on labs, and interactive sessions, participants will gain in-depth knowledge of Kubernetes fundamentals, explore best practices for deploying AI models on Kubernetes, and learn how to optimize and monitor machine learning (ML) and deep learning (DL) workloads in real-world settings. By the end of this course, participants will be equipped with the skills to successfully deploy and manage AI applications on Kubernetes and harness the full potential of this powerful integration.

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Learning Objectives:

By the end of the course, participants will be able to:

1. Understand Kubernetes Fundamentals: Grasp essential Kubernetes concepts and architecture, including containers, pods, and services, as well as Kubernetes clusters and nodes.
2. Deploy AI Models on Kubernetes: Learn how to containerize AI models and deploy them effectively on Kubernetes for scalable production workloads.
3. Optimize and Scale AI Workloads: Use advanced Kubernetes features, such as autoscaling and GPU acceleration, to optimize the performance of machine learning and deep learning models.
4. Monitor and Maintain AI Workflows: Implement tools and best practices for monitoring AI workloads in Kubernetes, ensuring stability, scalability, and efficient resource usage.
5. Leverage MLOps with Kubernetes: Understand MLOps concepts and integrate CI/CD pipelines to automate the model deployment, monitoring, and lifecycle management within Kubernetes.
6. Secure and Maintain Kubernetes Clusters: Gain insights into security considerations for deploying AI applications on Kubernetes and learn strategies for cluster maintenance and resource management.

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Part 1: Foundations of Kubernetes and AI Workloads

Module 1: Introduction to Kubernetes

• Overview of containers and container orchestration
• Kubernetes architecture: Nodes, clusters, and essential components (pods, deployments, services, namespaces)
• Setting up a local Kubernetes cluster (Minikube, Docker Desktop) vs. cloud-based Kubernetes (GKE, AKS, EKS)
• Hands-on Lab: Set up a basic Kubernetes cluster and deploy a sample application

Module 2: Containers for AI Workloads

• Introduction to containerizing AI/ML models with Docker
• Docker images for AI models and dependencies (TensorFlow, PyTorch, etc.)
• Managing large data and model files in containers
• Hands-on Lab: Containerize a simple AI model and push it to a container registry

Discussion:

• Best practices in Kubernetes for scalable AI applications.
• Assignment: Review a sample YAML configuration file for deploying an AI model to Kubernetes.

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Part 2: Deploying AI Models on Kubernetes

Module 1: Deployments and Service Management

• Creating and managing Kubernetes deployments for AI models
• Exposing services for AI models using LoadBalancer, NodePort, and Ingress
• Persistent storage and managing datasets in Kubernetes
• Hands-on Lab: Deploy a containerized AI model on Kubernetes and expose it via a service

Module 2: Advanced Resource Management for AI Workloads

• Configuring resources for CPU and memory allocation
• Leveraging GPU support in Kubernetes for AI models
• Understanding and setting limits and requests for resource management
• Hands-on Lab: Deploy a GPU-enabled AI model on a Kubernetes cluster

Discussion:

• Kubernetes resource management strategies for optimized AI performance.
• Assignment: Modify a YAML configuration to utilize GPU resources for a sample AI model.

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Part 3: Scaling, Optimization, and MLOps in Kubernetes

Module 1: Scaling and Optimization

• Autoscaling Kubernetes clusters for AI models (Horizontal Pod Autoscaling and Cluster Autoscaler)
• Optimizing performance with node pools and affinity/anti-affinity configurations
• Configuring AI models for distributed training using Kubernetes
• Hands-on Lab: Implement autoscaling for an AI model deployment on Kubernetes

Module 2: MLOps Integration

• Introduction to MLOps and CI/CD for AI applications
• Building CI/CD pipelines for AI model deployment on Kubernetes (using Jenkins, GitLab CI, or ArgoCD)
• Managing model versions and automated rollback mechanisms
• Hands-on Lab: Create a basic CI/CD pipeline for deploying AI models to Kubernetes

Discussion:

• Best practices for integrating MLOps with Kubernetes.
• Assignment: Draft a CI/CD pipeline configuration file for a sample AI model.

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Part 4: Monitoring, Security, and Cluster Maintenance

Module 1: Monitoring and Logging AI Workloads

• Monitoring tools for Kubernetes (Prometheus, Grafana) and logging (ELK stack, Fluentd)
• Setting up and interpreting metrics for AI workload performance
• Alerting and monitoring AI application health in real time
• Hands-on Lab: Implement monitoring and logging for an AI model on Kubernetes

Module 2: Security and Maintenance

• Securing Kubernetes clusters (RBAC, network policies, secrets management)
• Best practices for cluster maintenance and resource optimization
• Managing updates, scaling, and ensuring high availability
• Hands-on Lab: Secure a Kubernetes deployment and manage cluster updates