2022-12-07-fastapi.md

DIY FastAPI

• Goal: Learn about FastAPI to create an API that serves a Machine Learning model.
• Dates: from 7th to 13th December.
• Where: #project-of-the-week in DataTalks.Club (get in slack here: https://datatalks.club/slack.html)

For more information about the "Project of the Week" initiative at DataTalks.Club, see README.md.

If you want to receive reminders about this event, sign up here

Technologies

• FastAPI
• Scikit-Learn
• Tensorflow
• Pytorch

Note: this is a suggested list of technologies, you can chose alternatives instead

Plan

This is a proposed plan only, you don’t have to follow it day-by-day.

Day 1 (7 December, Wednesday)

• Come up with a project idea
• Select the dataset for your project. You can select a dataset from the suggested material below.
• Create a GitHub repository.
• Share your progress in Slack and on social media.

Suggestions: You can use a Machine Learning model that you might have created before or another project that you like:

• During a previous project-of-the-week:
  • Frontend for your ML model
  • Recommendation Systems
• An example of a Churn model.
• A list of some datasets that you can use for the project.
• Have a look at this sample project.

Day 2 (8 December, Thursday)

• Perform exploratory data analysis of your data in a Jupyter notebook.
• If you are using a previous project, then try to improve the model (or move on to Day 4).
• Push your changes to GitHub.
• Share your progress in Slack and on social media.

Day 3 (9 December, Friday)

• Create a baseline model and save it as a .pkl file.
• Start studying some introductory material about FastAPI.
• Push your changes to GitHub.
• Share your progress in Slack and on social media.

Suggested materials

• 📺 FastAPI Introduction - Build Your First Web App - Python Tutorial
• 🗒️ FastAPI First steps
• 🗒️ DataCamp Introduction to Fastapi

Found good materials? Create a PR with links!

Day 4 (10 December, Saturday)

• Continue learning about FastAPI
• Create a simple working example of an API:
  • Code along with this tutorial.
• Test your API using the interactive API documentation that FastAPI creates automatically with Swagger.
• (Optional) Write a Python script to test your API.
• (Optional) Test your API using curl or Postman.
• Push your changes to GitHub.
• Share your progress in Slack and on social media.

Suggested materials

• 📺 FastAPI Introduction - Build Your First Web App - Python Tutorial
• 🗒️ FastAPI First steps
• 📺 FastAPI Course for Beginners
• 🗒️ DataCamp Introduction to Fastapi

Found good materials? Create a PR with links!

Day 5 (11 December, Sunday)

• Create a new endpoint to serve the model you created (use the .pkl file you created on Day 3).
• Test your API using the interactive API documentation that FastAPI creates automatically with Swagger.
• Push your changes to GitHub.
• Share your progress in Slack and on social media.

Suggested materials

• 📺 Machine Learning Model Deployment Using FastAPI
• 🗒️ Introduction to Pydantic for FastAPI
• 🗒️ Fast API Features

Found good materials? Create a PR with links!

Day 6 (12 December, Monday)

• Add schemas in your API using the pydantic library (Base Model and Response Model).
• Add error handling to your API.
• (Optional) Create a Docker container for your project.
• Push your changes to GitHub.
• Share your progress in Slack and on social media.

Suggested materials

• 📺 Pydantic BaseModel and FAST API (code along)
• 🗒️ The Ultimate FastAPI Tutorial Part 5 - Basic Error Handling
• 🗒️ GET and POST requests using Python

Found good materials? Create a PR with links!

Day 7 (13 December, Tuesday)

• Continue exploring more about this topic.
• Write documentation for your project.
• (Optional) Deploy your API using Docker.
• Push your changes to GitHub.
• Share your progress in Slack and on social media.
• Give us feedback.
• Add the link to your project to this project of the week GitHub page.

Suggested materials

• 📺 Docker Tutorial For Beginners - How To Containerize Python Applications
• 💻 Serving Machine Learning Models GitHub repository
• 🗒️ How to Deploy a Machine Learning Model with FastAPI, Docker and Github Actions

Found good materials? Create a PR with links!

Projects

List of projects from our participants:

• https://github.com/lilianabs/fastapi-car-price-pred
• Churn-Prediction-Project
• https://github.com/MarcosMJD/ml-mango-classification
• Energy Efficiency Buildings
• ...
• (Create a PR)

(We will put the projects here after the event finishes)