Content

Pre-requisite in ML Pipelines needed

MLOps with Docker, GitLab, and AWS in 6 Days (2.5 Hours Each)

Module 1: Introduction to MLOps and Docker

Introduction to MLOps

• Definition and importance of MLOps

• Overview of the MLOps lifecycle

• Key concepts and components

• Introduction to Docker

• What is Docker and why use it in MLOps?

• Installing Docker and basic Docker commands

• Building Docker images

• Running and managing Docker containers

• Dockerfile basics: creating a Dockerfile for a machine learning project

• Hands-on Exercises

• Building and running a simple Docker container

• Dockerizing a basic ML application

Module 2: Version Control with GitLab and CI/CD Pipelines

• Introduction to GitLab

• GitLab overview and setup

• Git basics: cloning, branching, committing, and merging

• Using GitLab for version control in ML projects

• CI/CD with GitLab

• Introduction to CI/CD and its importance in MLOps

• Setting up GitLab CI/CD pipelines

• Writing `.gitlab-ci.yml` for ML projects

• Automating tests and builds with GitLab CI/CD

• Hands-on Exercises

• Creating a GitLab repository and setting up version control for an ML project

• Building a simple CI/CD pipeline to automate testing and deployment of a Dockerized ML model

Module 3: Deploying ML Models on AWS

• Introduction to AWS for MLOps

• Overview of AWS services relevant to MLOps

• Setting up AWS account and CLI

• Introduction to Amazon S3, EC2, and SageMaker

• Deploying Docker Containers on AWS

• Pushing Docker images to Amazon ECR (Elastic Container Registry)

• Running Docker containers on Amazon ECS (Elastic Container Service)

• Setting up and managing EC2 instances for ML model deployment

• Hands-on Exercises

• Deploying a Dockerized ML model on AWS ECS

• Storing and retrieving model data from S3

Module 4: Scaling and Monitoring ML Deployments

• Scaling ML Deployments

• Horizontal and vertical scaling strategies

• Using AWS Auto Scaling with ECS and EC2

• Best practices for scaling ML models in production

• Monitoring and Logging

• Importance of monitoring in MLOps

• Setting up monitoring and logging with AWS CloudWatch

• Integrating logging and monitoring into GitLab CI/CD pipelines

• Advanced Topics and Best Practices

• Security best practices for MLOps with Docker, GitLab, and AWS

• Managing secrets and configurations

• Cost optimization strategies on AWS

• Hands-on Exercises

• Implementing monitoring for an ML deployment on AWS

• Scaling an ML deployment based on usage patterns

• Final project: End-to-end MLOps pipeline from development to deployment and monitoring

Each day combines theoretical lessons with practical, hands-on exercises to ensure participants can apply the concepts learned in real-world scenarios. The course culminates in a comprehensive project that integrates all the tools and techniques covered.