- The Four Innovation Phases of Netflix’s Trillions Scale Real-time Data Infrastructure
- Machine Learning Production Myths, Chip Huyen
- Three Principles for Designing ML-Powered Products
- Continuous Delivery for Machine Learning
- Best Practices for ML Engineering - Google
- 150 successful ml models: 6 lessons learned at Booking.com
- Machine Learning How To: Reproducible and Trackable Training
- Data Drift Detection for Large Datasets - Evidently ai
- Feature Stores for ML
- Machine Learning Ops RoundUp - Substack
- Machine Learning Tools Landscape v2 - Chip Huyen
- Meet Michelangelo: Uber’s Machine Learning Platform
- Modeling Libraries Don't Matter
- Bag of Tricks for Optimizing ML Training Pipelines, Ntropy
- Design Patterns in Machine Learning Code and Systems, Eugene Yan
- Primer on Machine Learning for Fraud Protection, Stripe
- ML-Serving, Hamel Husain
- Kubernetes by Example
- Machine Learning Systems Design, Chip Huyen booklet
- Resources for DMLS Book by Chip Huyen github
- Stanford MLSys Seminar Series
- ML Systems Design Resources, GoogleDocs
- CS 329S: Machine Learning Systems Design, Chip Huyen
- Full Stack Deep Learning, UC Berkeley
- Practical Deep Learning for Coders, FastAI
- MLE for Production, Coursera
- Machine Learning System Design, Educative.io
- Introducing MLOps, How to Scale AI in the Enterprises
- Machine Learning Design Patterns Applications
- Designing Machine Learning Systems, Chip Huyen
- Infrastructure as Code, Kief Morris
- Efficient Deep Learning Book
- Rules of Machine Learning: Best Practices for ML Engineering
- Hidden Technical Debt in Machine Learning Systems - NIPS
- Model Evaluation, Model Selection, and Algorithm Selection in ML - Sebastian Raschka
- Improving Reproducibility in ML Research - NeurIPS 2019
- Machine Learning Operations (MLOps) Overview, Definition, and Architecture
- What is MLSys Design Interviews, Towards DataScience
- ML Systems Design Interview Guide, Patrick halina
- Grokking the ML Interview, Educative.io
- Principles of Good Machine Learning Systems Design
- Machine Learning Interviews
- ML Research and Production Pipelines
- Principles of Good ML Systems Design
- Career in Machine Learning
- ML Compilers: Bringing ML to the Edge
- Demo Day - Stanford ML Systems and Infra
- Debugging ML Models in Production
- Keeping up with ML in Production
- AI at Tesla - Full Stack Deep Learning
- Serving Machine Learning Models in Production - Cortex
- Human-Centric Machine Learning Infrastructure @ Netflix
- How to Evaluate MLOps Tools – Hamel Hussain
- TVM: An End to End Deep Learning Compiler Stack by Thiery Moreau (OctoML)
- Lavanya - Head of Growth W&Bs
- William Falcon talk at PyTorch Lightning