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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.
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Deployment and Operation
A booklet on machine learning systems design with exercises
Algorithms for outlier, adversarial and drift detection
Reproducible Rapid Research for Neural Architecture Search (NAS)
Always know what to expect from your data.
A thoughtful approach to hyperparameter management.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.
a tool that leverages rich metadata and lineage information in MLMD to build a model card
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.
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets --- https://arxiv.org/abs/2004.07999
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Library for exploring and validating machine learning data