In this workshop, we will go deeper into how to prototype a machine-learning project with Fast API. Fast API allows the creation API server with very little effort, it is easy to deploy a pre-trained model, but for models that require re-training, the challenge of when and how to retrain a model and update for a service in use becomes complicated. We will cover the aspect of delivering a pre-trained model and the design of re-training the model. This workshop will also provide suggestions for deploying the machine learning project so it can migrate from a prototype to a functional service in production.

Goal

The workshop aims to equip a data science team capability to convert their machine learning project into a prototype service using Fast API, at the end of the workshop, they will not just be able to deliver API calls to a pre-trained model, but they will also be able to design when to re-train and update the model and be ready to migrate the prototype into production.

Target audience

Data scientists who have little or no experience using Fast API or putting a machine learning model into production. This workshop will assume the audience already knows how to build and train a basic machine learning model (e.g. using Sci-kit learn).

Outline

Part 1 - Introduction to Fast APi and prediction on demand

• Understand the basics of Fast API
• Using a pre-trained model for prediction with API calls
• Validating the query parameters

Part 2 - Re-train and update models

• Problem with updating model: Race conditions
• Scheduled re-training
• Re-training on demand with Fast API

Part 3 - Machine learning model in production

• Fast API in docker containers
• Fast API on the cloud