Machine Learning DevOps Engineer

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Get introduced to clean code principles, why and when to use them, and the history of clean code. Then, see what you'll be able to build by the end of the course!

Learn coding best practices, such as clean and modular code, code efficiency, refactoring, documentation, and linting.

Version control is crucial for any coding project, but becomes even more important when working in teams. Another new area in working with teams is the code review, which you'll also learn about here.

Find more coding best practices here, such as handling errors, testing and logging, as well as addressing model drift in machine learning models.

Dive into reproducible model workflows and machine learning operations, learning about use cases, its history, and what you'll build at the end of the course.

Build out machine learning pipelines, as well as learning how to version data and model artifacts.

Come up with re-usable processes for performing exploratory data analysis (EDA), cleaning and pre-processing data, and segregating/splitting data.

Validate data through deterministic and non-deterministic testing, and look at handling different parameters with PyTest.

Write an inference pipeline, validate and choose your best performing models from experiments, and test your final model artifacts.

Write a full end-to-end pipeline, release the pipeline, and deploy with MLflow.

We'll introduce you to the course concepts of operationalizing our model, focusing on the ecosystem surrounding that model to successfully deploy it, and easily maintain it in production.

In this lesson, we will cover performance testing and preparing a model for production.

In this lesson, we will review git and then delve into Data Version Control (DVC) and the concepts of data provenance.

We cover the software engineering principles of automation, testing, and versioning. We put these into action using Continuous Integration and Continuous Delivery with Heroku and Github Actions.

Delve into FastAPI which leverages type hints to build a robust and self-documenting REST API. First, build out our API locally, test it, and the deploy to Heroku where you'll test it again live.

This lesson will talk about goals for the course, when to use ML model scoring and monitoring, stakeholders, the history of the field, and the tools and dependencies you need to be aware of.

This lesson will talk about model training and deployment. We’ll focus on automating the training and deployment process and making sure that the trained, deployed models are ready to be monitored.

This lesson will discuss model scoring and model drift, an important part of the continuous monitoring that makes sure your deployed model remains as accurate as possible.

There are problems that can come up in deployed projects. So this lesson will talk about diagnosing and fixing operational problems, a crucial part of the post-deployment machine learning process.

This lesson will discuss model reporting and monitoring with APIs which can be used as an automatic interface with your ML project.

Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.