Machine Learning > Software Engineering for Machine Learning

Python library focused on outlier, adversarial and drift detection.

Neural architecture search.

Data validation and testing with integration in pipelines.

A thoughtful approach to configuration management for machine learning projects.

A multi-type data labeling and annotation tool with standardized output format.

Linkedin fairness toolkit.

Sklearn-like framework for hyperparameter tuning and AutoML in deep learning projects.

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

Automatically detect bias in visual data sets.

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.

Library for exploring and validating machine learning data. Similar to Great Expectations, but for Tensorflow data.

Data Scientists in Software Teams: State of the Art and Challenges