Oreilly – Machine Learning Engineering in Action 2022-4

Oreilly – Machine Learning Engineering in Action 2022-4 Downloadly IRSpace

Apr 17, 2025 - 20:03 Updated: Aug 17, 2025 - 15:07

0 1

Oreilly – Machine Learning Engineering in Action 2022-4

Machine Learning Engineering in Action course. This course (excerpt from the book) provides you with key points, solutions, and applied design patterns to build usable, maintainable, and secure machine learning projects from initial idea to production.

What you will learn:

How to evaluate data science problems to find the most effective solution
Determining the scope of the machine learning project based on user expectations and budget
Process techniques that minimize waste of time and increase production speed
Project evaluation using standard prototyping and statistical validation
Choosing the right technologies and tools for your project
Creating a more understandable, maintainable and testable code base
Automating the process of fixing bugs and recording events

This course is suitable for people who:

They have the basic knowledge of machine learning and the basic principles of object-oriented programming.

Course details

Publisher: Oreilly
Instructor: Ben Wilson
Training level: beginner to advanced
Training duration: 14 hours 54 minutes

Course headings

Part 1. An introduction to machine learning engineering
Chapter 1. What is a machine learning engineer?
Chapter 2. Your data science could use some engineering
Chapter 3. Before you model: Planning and scoping a project
Chapter 4. Before you model: Communication and logistics of projects
Chapter 5. Experimentation in action: Planning and researching an ML project
Chapter 6. Experimentation in action: Testing and evaluating a project
Chapter 7. Experimentation in action: Moving from prototype to MVP
Chapter 8. Experimentation in action: Finalizing an MVP with MLflow and runtime optimization
Part 2. Preparing for production: Creating maintainable ML
Chapter 9. Modularity for ML: Writing testable and legible code
Chapter 10. Standards of coding and creating maintainable ML code
Chapter 11. Model measurement and why it’s so important
Chapter 12. Holding on to your gains by watching for drift
Chapter 13. ML development hubris
Part 3. Developing production machine learning code
Chapter 14. Writing production code
Chapter 15. Quality and acceptance testing
Chapter 16. Production infrastructure
Appendix A. Big O(no) and how to think about runtime performance
Appendix B. Setting up a development environment