Deploying ML Models with AWS Lambda (Serverless)
A low-cost, auto-scaling approach to serving ML models - Alexey Grigorev
This is the second workshop in our ML series on ML model deployment.
In this hands-on session, you’ll learn how to deploy machine learning models using serverless infrastructure, specifically AWS Lambda. Serverless deployment is a great alternative to managing your own infrastructure, ideal for lightweight models or infrequent inference.
This session walks you through packaging your model, creating a Lambda function, and exposing it via API Gateway.
What You'll Learn
What serverless means and when to use it
How AWS Lambda works for ML model serving
How to prepare your model for deployment with TensorFlow Lite
How to package your code and dependencies in a Docker image
How to expose your Lambda function using API Gateway
It will be a live demo with practical tips and a chance to ask your questions. This workshop gives you a real feel for how ML models are deployed in real-world environments.
Thinking About ML Zoomcamp? This workshop reflects topics from Module 9 in ML Zoomcamp, our free 4-month course that takes you from beginner to advanced ML engineer. It covers the fundamentals of ML, from regression and classification to deployment and deep learning.
The new cohort of the ML Zoomcamp starts on September 15, 2025. You can join it by registering here.
About the Speaker
Alexey Grigorev is the Founder of DataTalks.Club and creator of the Zoomcamp series.
Alexey is a seasoned software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS'17 Criteo Challenge.
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