The Best Flower Alternatives in 2026

FATE supports federated learning through an industrial grade framework.

The Datumbox framework is written in Java for developing machine learning and statistical applications.

The linfa project provides a machine learning framework written in Rust.

Hypernets offers a general automated machine learning framework for developing end to end AutoML toolkits in specific domains.

A JavaScript Native PyTorch-aligned Machine Learning Framework, built from scratch on WebGPU.

Build machine learning applications with Delta, an open-source framework implemented in Rust.

The fenn Python framework supports machine learning workflows and LLM agents, with trainers, templates, logging, and configuration management.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

Catniff provides JavaScript developers with a Torch-like deep learning framework.

DJL is a Java framework for running deep learning models across different engines.

PaddlePaddle provides a deep learning framework for training models across devices and platforms.

The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.

Build neural networks and support machine learning research with Sonnet, a library based on TensorFlow.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

Sagify describes its project as a way to work with large language models and machine learning more easily.

TPOT uses genetic programming to optimize machine learning pipelines in Python.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Merlin supports machine learning model management, deployment, and serving in Kubernetes environments.

Cog packages machine learning models into containers to support deployment across environments.

MLRun provides an open-source MLOps platform for managing machine learning applications, pipelines, and CI/CD delivery.

FLAML provides a fast Python library for automated machine learning and tuning.

The Feast repository describes an open source feature store for AI and machine learning projects.

Kubeflow provides a machine learning toolkit for deploying workflows on Kubernetes.

The Hydrosphere repository describes an MLOps platform for machine learning operations.