Build and deploy an ML REST API in 5 steps

Here is a hands-on FREE tutorial on MLOps that will help you grow as an ML engineer and go beyond notebooks.

Tutorial materials 📚🎬

All the code is open-source and available in this repository
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All the video lessons are on my Youtube channel
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This is what you will learn

You will learn to

• train an ML model prototype through careful experimentation, using CometML.

• deploy the model as a REST API, with Cerebrium.

• automate safe deployments, using GitHub actions and Comet ML Model Registry [COMING SOON]

 

Without further ado, let's get to work!

1. Set up your development environment

Learn to

• quickly bootstrap a project structure

• manage Python environments and dependencies with Python Poetry

• organize your code for maximum productivity

2. Generate training data

• In real-world projects there is no training dataset waiting for you.

• You need to generate it, starting from raw data, and ending up with clean pairs of (features, target).

This is what you will learn in this lecture.

3. Create a baseline model (without ML!)

• Before training any ML model you need to estimate a baseline performance, using a simple rule-based model.

• Simple historical values, or moving averages often work well.

4. Train ML models

• Building a good ML model is a very experimental process.

• Integrate your training script with a serverless experiment tracker like CometML so you experiment faster in a more disciplined way.

5. Deploy your model as a Serverless REST API

• Un-deployed models generate 0 business value.

• Make your model predictions available by deploying the model as a Serverless REST API using Cerebrium

What’s coming next?

In the following weeks I will release a few more videos, where you will learn how to automate deployments following CI/CD best-practices.