awesome-ml-engineering

MLOps

• Using GitHub Actions for MLOps & Data Science (June 2020) 📄
• Machine learning operations with GitHub Actions and Kubernetes - GitHub Universe 2019 📺
• GitHub Machine Learning Ops 🛠
• MLOps: Continuous delivery and automation pipelines in machine learning 📄
• Continuous Delivery for Machine Learning 📄
• ml-ops.org
• ML infracstructure at Spotify using TFX and Kubeflow (Dec 2019) 📄
• Building and Managing a Centralized Kubeflow Platform at Spotify (Nov 2019) 📺
• Engineering Systems for Real-Time Predictions @DoorDash (Dec 2018) 📺

ML systems design & Applications

• One Model At A Time: Integrating And Running Deep Learning Models In Production At EyeEm 📄
• A booklet on ML systems design with exercises 📚

CTR

• Large Scale CTR Prediction Lessons Learned @ Yelp (Aug 2016) 📺

Fraud detection

• Deep Learning Applications to Online Payment Fraud Detection @ PayPal (Apr 2019) 📺

ML model training

• Horovod, distributed training framework🛠
• Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools (Sep 2019) 🎓

DevOps

• Docker Mastery: with Kubernetes +Swarm from a Docker Captain 📺

Data

• Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning 🛠
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems 📚

GPU

• A Taste of GPU Compute 📺

Software engineering

• Hidden Technical Debt in Machine Learning Systems (2015) 🎓
• Nitpicking Machine Learning Technical Debt (2020) 📄
• A Philosophy of Software Design 📚

Bonus

• AlphaGo - The Movie 📺