How to Deploy Machine Learning Models to AWS Lambda using Docker & ECR

Take your model deployment to the next level by containerizing your machine learning workflows! In this tutorial, we demonstrate how to build a Docker image for a Random Forest model, push it to Amazon Elastic Container Registry (ECR), and deploy it using AWS Lambda. What you will learn in this video: - ECR Repository Setup: Creating a private repository and authenticating Docker via the AWS CLI. - Docker Workflow: Building, tagging, and pushing your ML model image to the cloud. - AWS Lambda Configuration: Creating a container-based Lambda function and assigning the correct IAM Roles (Basic Execution Role). - Troubleshooting & Optimization: How to fix common "Task Timed Out" errors by adjusting Lambda’s general configuration settings. - Interpreting Results: A breakdown of the JSON response and how the Iris dataset class indices (0, 1, 2) map to specific flower species. This approach is perfect for MLOps engineers and Data Scientists looking for a robust, scalable, and serverless way to serve model inferences.