AWS for Data Science: End-to-End ML Deployment on AWS (Lambda, Docker & API Gateway)(4/4)

Learn how to take your data science and machine learning models from a local notebook to a production-ready, serverless API on AWS. In this comprehensive lecture, we walk through the entire MLOps lifecycle using the Iris dataset as a case study.

We will cover how to package a Random Forest model using Docker, push it to Amazon Elastic Container Registry (ECR), deploy it using AWS Lambda, and expose it to the world using Amazon API Gateway. We will also cover essential troubleshooting steps, how to enable CORS for web applications, and setting up CloudWatch for logging and monitoring.

Key Concepts Covered:
Model Packaging: Containerizing Python ML code with Docker.
AWS Lambda: Deploying serverless inference functions using container images.
Amazon ECR: Managing and storing Docker images in the cloud.
API Gateway: Creating REST APIs to expose your ML model.
Monitoring: Using AWS CloudWatch to track performance and errors.
Best Practices: Cost optimization and security for ML workloads.

Timestamps:
0:00 Introduction to Model Deployment
1:25 Deployment Analogy: The Chef and the Restaurant
4:02 Overview of AWS Deployment Options (Lambda, ECS, SageMaker)
5:07 Hands-On Roadmap: The Iris Project
7:23 Setting up AWS CloudShell Environment
13:41 Training the Model Locally
14:29 Building the Docker Image & Pushing to Amazon ECR
19:52 Creating IAM Roles & AWS Lambda Function
24:13 Testing Lambda & Troubleshooting Timeout Errors
31:44 Updating Code for Human-Readable Predictions (Re-deployment)
48:04 Exposing the Model via Amazon API Gateway
56:30 Deploying the API & Testing with CURL
1:01:31 Testing API with a Local Python Client
1:04:22 Enabling CORS for Web Browser Access
1:09:31 Enabling Logging & Monitoring with CloudWatch
1:21:29 Best Practices: Monitoring & Cost Optimization