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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: Nov. 26, 2020, 3:13 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

Reproducible Rapid Research for Neural Architecture Search (NAS)

174
40
43d
n/a

Always know what to expect from your data.

2.86K
315
43d
Apache-2.0

A thoughtful approach to hyperparameter management.

113
5
27d
MIT

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

3.69K
345
6d
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.

98
8
72d
BSD-2-Clause

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

86
14
44d
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.

327
38
29d
Apache-2.0

Airflow is a platform to programmatically author, schedule and monitor workflows.

DVC is a data and ML experiments management tool.

Robust visualizations to aid in understanding machine learning datasets.

Replaces large files such as datasets with text pointers inside Git.

A platform for data scientists who want to build and experiment with ML pipelines.

platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework anf language agnostic, take a look at all the built-in integrations.

Experiment tracking tool bringing organization and collaboration to data science projects.

An inclusive movement to build an open, organized, online ecosystem for machine learning.

Machine Learning framework for Spark

TensorFlow's Visualization Toolkit.

An end-to-end platform for deploying production ML pipelines.

Experiment tracking, model optimization, and dataset versioning.