Machine Learning automation and tracking
Machine Learning Pipelines for Kubeflow
Uncover insights, surface problems, monitor, and fine tune your LLM
Segments.ai Python SDK
Open source platform for the machine learning lifecycle
Streamline your ML workflow
Test Suites for validating ML models & data
Powering Amazon custom machine learning chips
Python package built to ease deep learning on graph
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
A high-performance ML model serving framework, offers dynamic batching
The standard data-centric AI package for data quality and ML
TFDS is a collection of datasets ready to use with TensorFlow,
A Python Package to Tackle the Curse of Imbalanced Datasets in ML
TikZ figures for concepts in physics/chemistry/ML
Python package for AutoML on Tabular Data with Feature Engineering
The unified and scalable ML library for large-scale training
Feature Store for Machine Learning
Pythonic tool for running machine-learning/high performance workflows
Light-weight, flexible, expressive statistical data testing library
An MLOps framework to package, deploy, monitor and manage models
A toolkit to optimize ML models for deployment for Keras & TensorFlow
AutoGluon: AutoML for Image, Text, and Tabular Data
Serve machine learning models within a Docker container
Reference implementations of MLPerf™ training benchmarks