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Machine Learning guidelines

A curated list of articles and resources on software engineering best practices for building machine learning applications.

A curated list of articles that cover the software engineering best practices for building machine learning applications.

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Linked from 3 awesome lists

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Awesome Software Engineering for Machine Learning / Broad Overviews
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 🎓⭐

Awesome Software Engineering for Machine Learning / Data Management
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

Awesome Software Engineering for Machine Learning / Model Training
10 Best Practices for Deep Learning
Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement 🎓
Fairness On The Ground: Applying Algorithmic FairnessApproaches To Production Systems 🎓
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 🎓

Awesome Software Engineering for Machine Learning / Deployment and Operation
Best Practices in Machine Learning Infrastructure
Building Continuous Integration Services for Machine Learning 🎓
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 🎓

Awesome Software Engineering for Machine Learning / Social Aspects
Data Scientists in Software Teams: State of the Art and Challenges 🎓
Machine Learning Interviews 9,227 over 2 years ago
Managing Machine Learning Projects
Principled Machine Learning: Practices and Tools for Efficient Collaboration

Awesome Software Engineering for Machine Learning / Governance
A Human-Centered Interpretability Framework Based on Weight of Evidence 🎓
An Architectural Risk Analysis Of Machine Learning Systems
Beyond Debiasing
Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing 🎓
Inherent trade-offs in the fair determination of risk scores 🎓
Responsible AI practices ⭐
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Understanding Software-2.0 🎓

Awesome Software Engineering for Machine Learning / Tooling
Aim Aim is an open source experiment tracking tool
Airflow Programmatically author, schedule and monitor workflows
Alibi Detect 2,262 11 months ago Python library focused on outlier, adversarial and drift detection
Archai 468 about 1 year ago 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 10,054 11 months ago Data validation and testing with integration in pipelines
HParams 126 12 months ago 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 19,798 11 months ago A multi-type data labeling and annotation tool with standardized output format
LiFT 167 over 2 years ago Linkedin fairness toolkit
MLFlow Manage the ML lifecycle, including experimentation, deployment, and a central model registry
Model Card Toolkit 427 over 2 years ago 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 610 over 2 years ago 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
PyTorch Lightning 28,636 11 months ago The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate
REVISE: REvealing VIsual biaSEs 111 about 3 years ago Automatically detect bias in visual data sets
Robustness Metrics 466 over 1 year ago Lightweight modules to evaluate the robustness of classification models
Seldon Core 4,409 11 months ago An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models on Kubernetes
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
Tensorflow Data Validation (TFDV) 766 12 months ago Library for exploring and validating machine learning data. Similar to Great Expectations, but for Tensorflow data
Weights & Biases Experiment tracking, model optimization, and dataset versioning

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