Containerization of AI Applications with Docker and Kubernetes

Aim

Program Objectives

Program Structure

Module 1: Introduction to Containerization

• Overview of Containerization and Virtualization
• Benefits of Containers in AI/ML Workflows
• Key Concepts: Containers, Images, and Registries
• Introduction to Docker and Kubernetes

Module 2: Docker for AI Applications

• Installing Docker and Docker Basics
• Creating and Managing Docker Containers
• Building Docker Images for Machine Learning Models
• Dockerfile for AI Applications (Python, TensorFlow, PyTorch)
• Case Study: Containerizing a Simple AI Model

Module 3: Docker Compose for Multi-Container AI Applications

• Introduction to Docker Compose
• Defining and Managing Multi-Container Applications
• Linking AI Services (e.g., Model API, Database)
• Building and Orchestrating AI Applications with Docker Compose

Module 4: Introduction to Kubernetes for AI Applications

• Basics of Kubernetes Architecture (Pods, Nodes, Services)
• Setting Up a Kubernetes Cluster
• Deploying AI Applications in Kubernetes Pods
• Kubernetes vs. Docker: When to Use What?

Module 5: Scaling AI Applications with Kubernetes

• Horizontal and Vertical Scaling of AI Applications
• Managing Large-Scale AI Workloads with Kubernetes
• Auto-scaling AI Models Based on Load
• Monitoring and Managing Kubernetes Clusters

Module 6: Orchestrating AI Applications with Kubernetes

• Introduction to Kubernetes Deployments and Stateful Sets
• Load Balancing and Service Discovery for AI APIs
• Rolling Updates and Rollbacks in AI Models
• Case Study: Deploying an AI Model in Kubernetes

Module 7: CI/CD for AI with Docker and Kubernetes

• Integrating Docker and Kubernetes into CI/CD Pipelines
• Automating Model Packaging, Testing, and Deployment
• Tools for CI/CD in Kubernetes (Jenkins, GitLab CI, Argo)
• End-to-End AI Model Deployment Workflow

Module 8: Security and Monitoring in AI Containerization

• Security Best Practices for Docker and Kubernetes in AI Applications
• Securing AI Models and Data Pipelines in Containers
• Monitoring AI Applications with Kubernetes Dashboard and Prometheus
• Logging and Debugging AI Applications in Kubernetes

Who Should Enrol?

Program Outcomes

Fee Structure

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