A curated list of articles that cover software engineering (SE) best practices for building machine learning (ML) applications.
⭐️ Must-read
🎓 Scientific publication
- Broad Overviews
- Data Management
- Managing Model Training
- Deployment and Operation
- Social aspects
- Tooling
These publications cover all aspects.
- Software Engineering for Machine Learning: A Case Study 🎓⭐️
- Rules of Machine Learning: Best Practices for ML Engineering ⭐️
How to manage the data sets you use in machine learning.
- A Survey on Data Collection for Machine Learning A Big Data - AI Integration Perspective_2019 🎓
- Data management challenges in production machine learning
- How to organize data labelling for ML
- The curse of big data labeling and three ways to solve it
- The ultimate guide to data labeling for ML
How to organize your model training experiments.
- 10 Best Practices for Deep Learning
- How do you manage your Machine Learning Experiments?
- Machine learning: The high interest credit card of technical debt 🎓 ⭐️
- Machine Learning Testing: Survey, Landscapes and Horizons 🎓
- On human intellect and machine failures: Troubleshooting integrative machine learning systems 🎓
- Preparing and Architecting for Machine Learning
- Preliminary Systematic Literature Review of Machine Learning System Development Process
- Software development best practices in a deep learning environment 🎓
- Testing and Debugging in Machine Learning
- What Went Wrong and Why? Diagnosing Situated Interaction Failures in the Wild 🎓
How to deploy and operate your models in a production environment.
- Best Practices in Machine Learning Infrastructure
- Continuous Delivery for Machine Learning ⭐️
- Continuous Training for Production ML in the TensorFlow Extended (TFX) Platform 🎓
- Fairness Indicators: Scalable Infrastructure for Fair ML Systems 🎓
- Machine Learning Logistics
- Machine learning: Moving from experiments to production
- ML Ops: Machine Learning as an engineered disciplined
- Model Governance Reducing the Anarchy of Production 🎓
- ModelOps: Cloud-based lifecycle management for reliable and trusted AI
- Operational Machine Learning
- TFX: A tensorflow-based Production-Scale ML Platform 🎓
- Versioning for end-to-end machine learning pipelines 🎓
- What’s your ML Test Score? A rubric for ML production systems 🎓
How to organize teams and projects to ensure effective collaboration and accountability.
- How do teams work together on an automated machine learning project?
- Managing Machine Learning Projects
- Principled Machine Learning: Practices and Tools for Efficient Collaboration
- Team Data Science Process Documentation
- Responsible AI practices ⭐️
Tooling can make your life easier.
We only share open source tools, or commercial platforms that offer substantial free packages for research.
- Git Large File System (LFS)
- Data Version Control (DVC)
- Facets Overview / Facets Dive
- MLFlow
- Kubeflow
- Airflow
- Tensorflow Extended (TFX)
- Spark Machine Learning
- Sacred
- TensorBoard
- Weights & Biases
- Neptune.ai
Contributions welcome! Read the contribution guidelines first.
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