Software Engineering for Machine Learning

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Aim is an open source experiment tracking tool.

Programmatically author, schedule and monitor workflows.

Python library focused on outlier, adversarial and drift detection.

Neural architecture search.

DVC is a data and ML experiments management tool.

Robust visualizations to aid in understanding machine learning datasets.

A toolkit to assess and improve the fairness of machine learning models.

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

Data validation and testing with integration in pipelines.

A thoughtful approach to configuration management for machine learning projects.

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

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

Linkedin fairness toolkit.

Manage the ML lifecycle, including experimentation, deployment, and a central model registry.

Streamlines and automates the generation of model cards; for model documentation.

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

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

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

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.

Spark’s ML library consisting of common learning algorithms and utilities.

TensorFlow's Visualization Toolkit.

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

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

Experiment tracking, model optimization, and dataset versioning.