Overview

Automate and streamline the deployment of machine learning models, combining software engineering principles with machine learning workflows. This program covers key skills like writing production-ready code, creating reproducible workflows, and building automated deployment pipelines. Through real-world projects, you’ll develop scalable pipelines, version control for data and models, monitor model performance, and implement CI/CD processes to ensure resilient, maintainable systems. Ideal for data scientists aiming to operationalize their models, these skills empower you to build effective, long-term solutions tailored for industry demands.

Syllabus

Welcome to the Machine Learning DevOps Engineer Nanodegree

Welcome to Udacity! We're excited to share more about your nanodegree and start this journey with you!
In this course, you will learn more about the pre-requisites, structure of the program, and getting started!

Clean Code Principles

Develop skills that are essential for deploying production machine learning models. First, you will put your coding best practices on auto-pilot by learning how to use PyLint and AutoPEP8. Then you will further expand your git and Github skills to work with teams. Finally, you will learn best practices associated with testing and logging used in production settings in order to ensure your models can stand the test of time.

Building a Reproducible Model Workflow

This course empowers the students to be more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. In particular, it teaches the fundamentals of MLops and how to: a) create a clean, organized, reproducible, end-to-end machine learning pipeline from scratch using MLflow b) clean and validate the data using pytest c) track experiments, code, and results using GitHub and Weights & Biases d) select the best-performing model for production and e) deploy a model using MLflow. Along the way, it also touches on other technologies like Kubernetes, Kubeflow, and Great Expectations and how they relate to the content of the class.

Deploying a Scalable ML Pipeline in Production

This course teaches students how to robustly deploy a machine learning model into production. En route to that goal students will learn how to put the finishing touches on a model by taking a fine grained approach to model performance, checking bias, and ultimately writing a model card. Students will also learn how to version control their data and models using Data Version Control (DVC). The last piece in preparation for deployment will be learning Continuous Integration and Continuous Deployment which will be accomplished using GitHub Actions and Heroku, respectively. Finally, students will learn how to write a fast, type-checked, and auto-documented API using FastAPI.

ML Model Scoring and Monitoring

This course will help students automate the devops processes required to score and re-deploy ML models. Students will automate model training and deployment. They will set up regular scoring processes to be performed after model deployment, and also learn to reason carefully about model drift, and whether models need to be retrained and re-deployed. Students will learn to diagnose operational issues with models, including data integrity and stability problems, timing problems, and dependency issues. Finally, students will learn to set up automated reporting with API’s.

Congratulations!

Congratulations on finishing your program!

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