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.
Books
- The Design of Everyday Things
- Thinking in Systems
- A Philosophy of Software Design
- Ethics and Data Science
- AI Engineering
- Deep Learning for Coders with fastai and PyTorch
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Hands-On Generative AI with Transformers and Diffusion Models
- Designing Machine Learning Systems
- Machine Learning Production Systems
- Machine Learning Systems
- Machine Learning Design Patterns
- Designing Data-Intensive Applications
Courses
- Full Stack Deep Learning
- Full Stack LLM Bootcamp
- DeepLearning.AI Natural Language Processing Specialization
- TinyML and Efficient Deep Learning Computing
Papers
- Hidden Technical Debt in Machine Learning Systems
- What's your ML Test Score? A rubric for ML production system
- Challenges in Deploying Machine Learning: a Survey of Case Studies
- Operationalizing Machine Learning: An Interview Study