awesome-mlops

A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.

Provides advanced parallelism for analytics, enabling performance at scale for the tools you love.

Deep learning optimization library that makes distributed training easy, efficient, and effective.

Python distributed computing library for modern computer clusters.

Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

Distributed linear algebra framework and mathematically expressive Scala DSL.

Open-source implementation of Google Vizier for hyper parameters tuning.

A very simple wrapper for convenient hyperparameter optimization.

Distributed Asynchronous Hyperparameter Optimization in Python.

Kubernetes-based system for hyperparameter tuning and neural architecture search.

Easy-to-use, scalable hyperparameter optimization framework.

A framework for performing reproducible AI and ML for Weights and Biases.

A super-easy way to record, search and compare 1000s of ML training runs.

Library of ML-Engineering tools for rapid prototyping and experiment management.

Track your datasets, code changes, experimentation history, and models.

Open source experiment tracking, pipeline automation, and hyperparameter tuning.

Version control for machine learning with support to Amazon S3 and Google Cloud Storage.

Keynotes and Panels

A comprehensive set of fairness metrics for datasets and machine learning models.

A Python package to assess and improve fairness of machine learning models.

Platform to programmatically author, schedule, and monitor workflows.

Batch workflow job scheduler created at LinkedIn to run Hadoop jobs.

A data orchestrator for machine learning, analytics, and ETL.

Framework that allows for the distributed processing of large data sets across clusters.