Containerization of AI Applications with Docker and Kubernetes
Deploy and Scale AI Applications with the Power of Docker and Kubernetes
Skills you will gain:
The program covers the complete process of containerizing AI models and applications using Docker and orchestrating them with Kubernetes. Participants will learn the fundamentals of containerization, deploying AI models, managing dependencies, and scaling AI applications using Kubernetes in both on-premise and cloud environments.
Aim: To teach participants how to containerize AI applications using Docker and Kubernetes for scalable deployment and management. This program provides practical knowledge for ensuring that AI models and applications can run consistently across multiple environments with scalability, security, and ease of orchestration.
Program Objectives:
- Learn how to containerize AI models and applications using Docker.
- Understand Kubernetes orchestration for scaling and managing AI applications.
- Implement real-world deployment pipelines for AI solutions.
- Gain proficiency in building Dockerfiles, managing dependencies, and scaling applications.
- Learn best practices for handling large-scale AI applications in Kubernetes.
What you will learn?
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
Intended For :
AI engineers, data scientists, cloud architects, DevOps professionals looking to deploy scalable AI applications using Docker and Kubernetes.
Career Supporting Skills