Awesome Machine Learning Software Engineering – Massive Collection of Resources

⭐ Must-read

🎓 Scientific publication

Contents

• Broad Overviews
• Data Management
• Model Training
• Deployment and Operation
• Social Aspects
• Governance
• Tooling

Broad Overviews

These resources cover all aspects.

• AI Engineering: 11 Foundational Practices ⭐
• Best Practices for Machine Learning Applications
• Engineering Best Practices for Machine Learning ⭐
• Hidden Technical Debt in Machine Learning Systems 🎓⭐
• Rules of Machine Learning: Best Practices for ML Engineering ⭐
• Software Engineering for Machine Learning: A Case Study 🎓⭐

Data Management

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
• Data Validation for 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

Model Training

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 🎓
• Nitpicking Machine Learning Technical Debt
• 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 🎓

Deployment and Operation

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
• Scaling Machine Learning as a Service🎓
• TFX: A tensorflow-based Production-Scale ML Platform 🎓
• The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction 🎓
• Underspecification Presents Challenges for Credibility in Modern Machine Learning 🎓
• Versioning for end-to-end machine learning pipelines 🎓

Social Aspects

How to organize teams and projects to ensure effective collaboration and accountability.

• Data Scientists in Software Teams: State of the Art and Challenges 🎓
• Machine Learning Interviews
• Managing Machine Learning Projects
• Principled Machine Learning: Practices and Tools for Efficient Collaboration

Governance

• An Architectural Risk Analysis Of Machine Learning Systems
• Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing 🎓
• Responsible AI practices ⭐
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Tooling

Tooling can make your life easier.

We only share open source tools, or commercial platforms that offer substantial free packages for research.

• Airflow – Programmatically author, schedule and monitor workflows.
• Archai – Neural architecture search.
• Data Version Control (DVC) – DVC is a data and ML experiments management tool.
• Facets Overview / Facets Dive – Robust visualizations to aid in understanding machine learning datasets.
• FairLearn – A toolkit to assess and improve the fairness of machine learning models.
• Git Large File System (LFS) – Replaces large files such as datasets with text pointers inside Git.
• Great Expectations – Data validation and testing with integration in pipelines.
• HParams – A thoughtful approach to configuration management for machine learning projects.
• Kubeflow – A platform for data scientists who want to build and experiment with ML pipelines.
• Label Studio – A multi-type data labeling and annotation tool with standardized output format.
• LiFT – Linkedin fairness toolkit.
• MLFlow – Manage the ML lifecycle, including experimentation, deployment, and a central model registry.
• Model Card Toolkit – Streamlines and automates the generation of model cards; for model documentation.
• Neptune.ai – Experiment tracking tool bringing organization and collaboration to data science projects.
• Neuraxle – Sklearn-like framework for hyperparameter tuning and AutoML in deep learning projects.
• OpenML – An inclusive movement to build an open, organized, online ecosystem for machine learning.
• Robustness Metrics – Lghtweight modules to evaluate the robustness of classification models.
• Spark Machine Learning – Spark’s ML library consisting of common learning algorithms and utilities.
• TensorBoard – TensorFlow’s Visualization Toolkit.
• Tensorflow Extended (TFX) – An end-to-end platform for deploying production ML pipelines.
• Weights & Biases – Experiment tracking, model optimization, and dataset versioning.