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
Last synced: 15 days ago
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Broad Overviews
- AI Engineering: 11 Foundational Practices
- Best Practices for Machine Learning Applications
- Engineering Best Practices for Machine Learning
- Rules of Machine Learning: Best Practices for ML Engineering
- Software Engineering for Machine Learning: A Case Study
- AI Engineering: 11 Foundational Practices
- AI Engineering: 11 Foundational Practices
- Hidden Technical Debt in Machine Learning Systems
- Best Practices for Machine Learning Applications
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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
- Automating Large-Scale Data Quality Verification
- The Data Linter: Lightweight, Automated Sanity Checking for ML Data Sets
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Deployment and Operation
- 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
- The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
- Underspecification Presents Challenges for Credibility in Modern Machine Learning
- 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
- 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
- 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
- ModelOps: Cloud-based lifecycle management for reliable and trusted AI
- Scaling Machine Learning as a Service
- TFX: A tensorflow-based Production-Scale ML Platform
- Versioning for end-to-end machine learning pipelines
- Building Continuous Integration Services for Machine Learning
- ML Ops: Machine Learning as an engineered disciplined
- Best Practices in Machine Learning Infrastructure
- Fairness Indicators: Scalable Infrastructure for Fair ML Systems
- Machine learning: Moving from experiments to production
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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
- Inherent trade-offs in the fair determination of risk scores
- Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
- Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
- Responsible AI practices
- Understanding Software-2.0
- A Human-Centered Interpretability Framework Based on Weight of Evidence
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Model Training
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- Pitfalls of supervised feature selection
- How do you manage your Machine Learning Experiments?
- On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
- 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
- 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
- Pitfalls and Best Practices in Algorithm Configuration
- 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
- 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
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Social Aspects
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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.
- Kubeflow - A platform for data scientists who want to build and experiment with ML pipelines.
- MLFlow - Manage the ML lifecycle, including experimentation, deployment, and a central model registry.
Programming Languages
Categories
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Keywords
machine-learning
7
deep-learning
6
mlops
3
python
3
data-science
2
hyperparameters
2
pipeline
2
hyperparameter-optimization
2
pytorch
2
data-profiling
1
data-quality
1
data-unit-tests
1
datacleaner
1
datacleaning
1
dataquality
1
dataunittest
1
eda
1
exploratory-analysis
1
exploratory-data-analysis
1
exploratorydataanalysis
1
pipeline-debt
1
pipeline-testing
1
pipeline-tests
1
aiops
1
data-visualization
1
model-cards
1
responsible-ai
1
responsible-ml
1
tensorflow
1
transparency
1
adversarial
1
anomaly
1
concept-drift
1
data-drift
1
detection
1
drift-detection
1
images
1
outlier
1
semi-supervised-learning
1
tabular-data
1
text
1
time-series
1
unsupervised-learning
1
cleandata
1
data-engineering
1
data-profilers
1
python3
1
annotation
1
annotation-tool
1
annotations
1