Learn Python for Data Science in this Beginners’ Day Workshop

Would you like to learn to code but don’t know where to start? Taking your first steps in programming can seem like an impossible task, so we’ve decided to put on a workshop to show beginners how it can be done and share our passion for the world of data science!

In this workshop, you will learn the basics of programming in Python, as well as how to use tools such as Jupyter notebooks to analyse data.

You will be learning in small groups, each with an assigned mentor to guide you through the workshop materials and answer your questions. You can expect plenty of exercises, as well as inspiring talks from those who were once in your shoes.

Learn Python for Data Science in this Beginners’ Day Workshop

Would you like to learn to code but don’t know where to start? Taking your first steps in programming can seem like an impossible task, so we’ve decided to put on a workshop to show beginners how it can be done and share our passion for the world of data science!

In this workshop, you will learn the basics of programming in Python, as well as how to use tools such as Jupyter notebooks to analyse data.

You will be learning in small groups, each with an assigned mentor to guide you through the workshop materials and answer your questions. You can expect plenty of exercises, as well as inspiring talks from those who were once in your shoes.

One of the challenges for a machine learning project is to deploy it. Fast API provides a fast and easy way to deploy a prototype with less software development expertise and yet allow it to be developed into a professional web service. We will look at how to do it.

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