Deploying a Machine Learning Model to the Cloud Using AWS Lambda
summary
Shows how to take a trained Jupyter/Python model, upload it to S3 and expose it as a live HTTP prediction endpoint by deploying AWS Lambda functions behind API Gateway using CloudFormation or the Serverless Framework. Covers packaging and runtime constraints (zip vs unzipped limits, Lambda layers, Python 3.6), concurrency/testing considerations (thousands of concurrent executions, test events), and demonstrates a working end-to-end API that returns predictions.
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chapters (11)
- 0:00Deploying ML on AWS Lambda
- 10:35AWS Lambda for ML benefits
- 17:14Setting up ML model
- 25:49S3 for model storage
- 34:26Lambda triggers and events
- 41:13Deploy with CloudFormation
- 50:58Test and debug ML service
- 1:00:11IAM policies for security
- 1:09:09Final deployment steps
- 1:16:45Optimize Lambda performance
- 1:23:03Production best practices
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