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awesome-seml
A curated list of articles that cover the software engineering best practices for building machine learning applications.
https://github.com/SE-ML/awesome-seml
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Model Training
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- 10 Best Practices for Deep Learning
- 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
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Preparing and Architecting for Machine Learning
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Software development best practices in a deep learning environment
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
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Deployment and Operation
- ML Ops: Machine Learning as an engineered disciplined
- 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
- 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
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- Versioning for end-to-end machine learning pipelines
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- Fairness Indicators: Scalable Infrastructure for Fair ML Systems
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
- ML Ops: Machine Learning as an engineered disciplined
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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
- AI Engineering: 11 Foundational Practices
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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
- How to organize data labelling for ML
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Social Aspects
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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
- Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
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Tooling
- Aim - Aim is an open source experiment tracking tool.
- Airflow - Programmatically author, schedule and monitor workflows.
- Data Version Control (DVC) - DVC is a data and ML experiments management tool.
- 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.
- 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.
- MLFlow - Manage the ML lifecycle, including experimentation, deployment, and a central model registry.
- Neptune.ai - Experiment tracking tool bringing organization and collaboration to data science projects.
- OpenML - An inclusive movement to build an open, organized, online ecosystem for machine learning.
- PyTorch Lightning - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
- Spark Machine Learning - Spark’s ML library consisting of common learning algorithms and utilities.
- Weights & Biases - Experiment tracking, model optimization, and dataset versioning.
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