The Best ormb Alternatives in 2026

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

ModelDB tracks machine learning model versions, metadata, and experiments.

Flexible, high-performance serving system for machine learning models.

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

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

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

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

This Docker-based starter kit provides a runtime for machine learning work in Jupyter notebooks.

The fuku-ml project provides a machine learning library described as simple to use.

Apache SINGA supports distributed deep learning through a system and library for model training.

DeepDetect provides a deep learning server and command-line interface for Torch and TensorRT.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

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

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

PrimeHub offers an open-source platform for managing machine learning operations and workflows.

Use Aeromancy to build reproducible machine learning workflows and projects.

Model deployment for AI applications is supported by nndeploy, a framework described as easy to use and high performance.

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

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

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

Machine learning operations and large language model operations are the focus of Starwhale’s platform.

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

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

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