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Awesome Software Engineering for Machine Learning

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

Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.

Last Update: Jan. 27, 2023, 3:02 p.m.

Thank you SE-ML & contributors
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SE-ML/awesome-seml

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Broad Overviews

Data Management

Model Training

Deployment and Operation

Social Aspects

Governance

Tooling

Algorithms for outlier, adversarial and drift detection

922
96
1y 85d
Apache-2.0

Reproducible Rapid Research for Neural Architecture Search (NAS)

312
61
1y 84d
n/a

Always know what to expect from your data.

5.61K
734
1y 84d
Apache-2.0

A thoughtful approach to hyperparameter management.

122
8
2y 89d
MIT

Label Studio is a multi-type data labeling and annotation tool with standardized output format

6.87K
758
1y 85d
Apache-2.0

The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.

144
17
1y 10m
BSD-2-Clause

a tool that leverages rich metadata and lineage information in MLMD to build a model card

232
37
1y 90d
Apache-2.0

A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

472
51
1y 86d
Apache-2.0

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

15.91K
1.96K
1y 84d
Apache-2.0

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets --- https://arxiv.org/abs/2004.07999

71
13
1y 88d
MIT
356
16
1y 4m
Apache-2.0

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

2.64K
589
1y 85d
Apache-2.0

Library for exploring and validating machine learning data

589
114
1y 92d
Apache-2.0