A curated list of articles that cover software engineering (SE) best practices for building machine learning (ML) applications.
⭐️ Must-read
🎓 Scientific publication
Based on this literature, we compiled a survey on the adoption of sofware engineering practices for applications with machine learning components.
Feel free to take and share the survey!
These publications cover all aspects.
- Best Practices for Machine Learning Applications
- Hidden Technical Debt in Machine Learning Systems 🎓⭐️
- Rules of Machine Learning: Best Practices for ML Engineering ⭐️
- Software Engineering for Machine Learning: A Case Study 🎓⭐️
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 🎓
- Automating Large-Scale Data Quality Verification 🎓
- 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 Data Linter: Lightweight, Automated Sanity Checking for ML Data Sets 🎓
- The ultimate guide to data labeling for ML
How to organize your model training experiments.
- 10 Best Practices for Deep Learning
- Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement 🎓
- How do you manage your Machine Learning Experiments?
- Machine Learning Testing: Survey, Landscapes and Horizons 🎓
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach 🎓⭐️
- On human intellect and machine failures: Troubleshooting integrative machine learning systems 🎓
- Pitfalls and Best Practices in Algorithm Configuration 🎓
- Pitfalls of supervised feature selection 🎓
- 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 🎓
- The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction 🎓
- Versioning for end-to-end machine learning pipelines 🎓
How to organize teams and projects to ensure effective collaboration and accountability.
- Data Scientists in Software Teams: State of the Art and Challenges 🎓
- How do teams work together on an automated machine learning project?
- Machine Learning Interviews
- 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.
- Airflow
- Auto-PyTorch
- Ax
- Data Version Control (DVC)
- Facets Overview / Facets Dive
- Git Large File System (LFS)
- HParams
- Kubeflow
- Label Studio
- MLFlow
- Neptune.ai
- Neuraxle
- OpenML
- Spark Machine Learning
- Sacred
- TensorBoard
- Tensorflow Extended (TFX)
- Weights & Biases
Contributions welcomed! Read the contribution guidelines first.
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