Readings in Machine Learning Engineering
One of my goals this year, and every year, is to become a better machine learning engineer. Right now, I'm focusing on basics and best practices — reviewing the fundamentals, filling in knowledge gaps, and learning the recommended way to do things. Here is a collection of some of my learning resources.
Courses
- Machine Learning Crash Course
- Machine Learning Specialization
- Deep Learning Specialization
- Become a Machine Learning Engineer
- Machine Learning Engineering for Production (MLOps)
- Full Stack Deep Learning
Books
- The Design of Everyday Things
- Thinking in Systems
- A Philosophy of Software Design
- Machine Learning Design Patterns
- Designing Machine Learning Systems
- Designing Data-Intensive Applications
- Ethics and Data Science
- Fluent Python
- Pro Git
Papers
- Hidden Technical Debt in Machine Learning Systems
- What’s your ML Test Score? A rubric for ML production system
- Towards ML Engineering: A brief history of TensorFlow Extended (TFX)
- Challenges in Deploying Machine Learning: a Survey of Case Studies
- Operationalizing Machine Learning: An Interview Study
- Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
- Model Cards for Model Reporting
Articles
- Teach Yourself Programming in Ten Years
- Education at Bat: Seven Principles for Educators
- How I learn machine Learning
- Why and How to Read Papers
- Making sense of MVP (Minimum Viable Product) – and why I prefer Earliest Testable/Usable/Lovable