Introduction to MLOps

You'll learn how to create APIs , containerize your machine learning models, and deploy them using MLOps.




Hours per week


Live Sessions

Feb 12, 2024

Next Date


The objective of this course is to provide participants with a comprehensive understanding of how to leverage FastAPI for building and deploying machine learning models. Participants will learn the basics of FastAPI and its advantages as a web framework. They will gain practical knowledge on how to build machine learning models using FastAPI and create APIs to serve them. Additionally, participants will explore the benefits of containerizing machine learning models with Docker and learn how to deploy their models using FastAPI and Docker. By the end of the course, participants will be equipped with the necessary skills to effectively build, containerize, and deploy machine learning models using FastAPI, enabling them to develop robust and scalable machine learning applications for real-world use cases.


  • Experience with Python programming language
  • Familiarity with machine learning fundamentals


Overview of FastAPI

In this section, we'll cover the basics of FastAPI and its advantages over other web frameworks.

Building Machine Learning Models with FastAPI

Here, we'll dive into building machine learning models with FastAPI and creating APIs to serve them.

Containerizing Machine Learning Models with Docker

In this section, we'll discuss the benefits of using Docker to containerize your machine learning models and how to get started with Docker.

Deploying Machine Learning Models with FastAPI

Finally, we'll take a look at how to deploy your machine learning models using FastAPI and Docker.


Sebastian Vazquez

Sebastian Vazquez

Current CTO of JA-VAZ, he teaches programs such as Git for Data projects.

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