Program Structure

Module 1: Introduction to Containerization

• What is containerization and why is it important for AI applications?
• Understanding Docker and Kubernetes: Overview and architecture.
• The benefits of using containers for AI applications: scalability, portability, and environment consistency.

Module 2: Docker for AI Applications

• Introduction to Docker: What it is and how it works.
• Creating Docker containers for AI applications: Building Dockerfiles, managing dependencies, and using pre-built images.
• How to containerize machine learning models and Python-based AI applications.
• Hands-on project: Build a Docker image for an AI application or model and run it in a container.

Module 3: Introduction to Kubernetes

• What is Kubernetes and how does it help manage containerized applications?
• Understanding the architecture of Kubernetes: Pods, nodes, services, and clusters.
• How Kubernetes enables scalability and management of AI containers in production.
• Hands-on project: Set up a Kubernetes cluster and deploy a containerized AI application.

Module 4: Docker and Kubernetes Workflow

• How to build, push, and pull Docker images to and from Docker Hub or private registries.
• Creating and managing Kubernetes pods and services for AI applications.
• Understanding Kubernetes deployments: Managing replicas, rollouts, and updates.
• Hands-on project: Deploy a machine learning model to Kubernetes and scale it using Kubernetes deployment strategies.

Module 5: Networking and Storage in Kubernetes

• How to expose AI applications and containerized models to the outside world using Kubernetes services.
• Managing persistent storage in Kubernetes for AI applications with stateful applications and volumes.
• How to configure load balancing in Kubernetes for high-availability and scalability.
• Hands-on project: Set up a Kubernetes network for containerized AI models and implement persistent storage.

Module 6: Continuous Integration and Deployment (CI/CD) with Docker and Kubernetes

• Overview of CI/CD for containerized AI applications.
• Setting up automated pipelines to build, test, and deploy Docker images to Kubernetes.
• Using tools like Jenkins, GitLab CI, and GitHub Actions for automation in AI model deployment.
• Hands-on project: Implement a CI/CD pipeline for deploying an AI model using Docker and Kubernetes.

Module 7: Model Monitoring and Feedback Loops

• How to monitor containerized AI applications using Kubernetes dashboards and Prometheus.
• Implementing auto-scaling and load balancing in Kubernetes to handle spikes in traffic or model predictions.
• Setting up model drift detection and retraining in an automated workflow for scaling models in production.
• Hands-on project: Set up monitoring and auto-scaling for your Kubernetes-deployed AI application.

Module 8: Best Practices for Containerizing AI Models

• Best practices for optimizing Docker images for AI models: reducing image size and improving efficiency.
• Security considerations when containerizing AI models: Docker security best practices and securing Kubernetes clusters.
• Ensuring compliance and privacy in AI containerization workflows.
• Hands-on project: Implement security features in your Docker and Kubernetes AI deployment pipeline.

Module 9: Advanced Topics in Kubernetes for AI

• Using Kubernetes for multi-cloud deployments and handling hybrid cloud architectures for AI applications.
• Integrating Kubernetes with AI services and data pipelines for end-to-end machine learning workflows.
• Exploring serverless Kubernetes architectures for event-driven AI applications.

Final Project

• Design and implement an end-to-end AI application deployment using Docker and Kubernetes.
• Containerize an AI model, deploy it to Kubernetes, scale it as necessary, and ensure it is production-ready.
• Example projects: Build and deploy a real-time image classification system, speech recognition system, or predictive analytics model using Docker and Kubernetes.