awesome-seml
by SE-ML
A curated list of articles that cover the software engineering best practices for building machine learning applications.
AI summary
Machine Learning guidelines
A curated list of articles and resources on software engineering best practices for building machine learning applications.
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What's in the list
84 links in 7 sections, with live GitHub stats.activeno commit in 2y
Broad Overviews
Data Management
Model Training
Deployment and Operation
Social Aspects
Machine Learning InterviewsGovernance
Tooling
- Aim
Aim is an open source experiment tracking tool
- Airflow
Programmatically author, schedule and monitor workflows
Alibi Detect
Python library focused on outlier, adversarial and drift detection
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
PyTorch Lightning
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate
REVISE: REvealing VIsual biaSEs
Automatically detect bias in visual data sets
Robustness Metrics
Lightweight modules to evaluate the robustness of classification models
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
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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