0.6. Resources

What supplementary resources are available for this course?

This course provides several resources to enhance your learning experience.

MLOps Python Package: This companion project serves as a concrete example of a well-structured MLOps codebase. It utilizes the same dataset as the course, offering a complete picture of what your final project could look like.
Cookiecutter MLOps Package: To help you hit the ground running, this cookiecutter template generalizes the concepts from the course and the MLOps Python Package. It allows you to quickly scaffold a new MLOps project with a production-ready structure.
Personal Blog Posts: For deeper insights into specific topics, the course creators have published articles on subjects like configuring Visual Studio Code for MLOps and using Pydantic for data validation. These posts provide practical advice to sharpen your skills.

How can I contribute to the course resources?

This course is open-source, and we welcome contributions that enhance its value for all learners. If you have discovered a valuable tool, library, or article, or if you have created your own resources inspired by the course, we encourage you to share them.

To suggest a new resource, please create an issue on the course's GitHub repository. Your contributions help your peers and play a vital role in keeping the course material current with the latest MLOps best practices.

What are some key industry-recognized MLOps resources?

Beyond the materials provided directly with this course, several external resources are highly recommended for a comprehensive understanding of MLOps.

MLOps Python Package: A reference implementation for a production-ready MLOps project.
LLMOps Python Package: An LLM-focused version of the MLOps Python Package, tailored for Large Language Model Operations.
Cookiecutter MLOps Package: A project template to quickly start new MLOps projects with a standardized structure.
MLOps: Continuous delivery and automation pipelines in machine learning: A foundational article from Google Cloud that defines MLOps and outlines its core principles and stages.
The Big Book of MLOps: A comprehensive guide from Databricks covering the entire MLOps lifecycle, from data preparation to model deployment and monitoring.
Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning: Another excellent whitepaper from Google, offering a framework and practical advice for implementing MLOps.
AWS Machine Learning Lens: Part of the AWS Well-Architected Framework, this guide provides best practices for designing and operating machine learning workloads on AWS.