Self-Hosted Experiment Tracking and MLOps

Kubeflow is a self-hosted AI platform for teams developing and running machine learning workloads on Kubernetes. Its modular projects cover interactive development, training and the work of moving models into production.

SwanLab is an open-source AI experiment tracking platform for researchers and model training teams. It brings training results into one place so you can spot problems and compare runs before choosing your next experiment.

OpenResearch is an MIT-licensed, open-source workspace for people using AI coding agents to conduct research. It gives each research direction a separate agent session and isolated Git worktree, so experiments can proceed in parallel without sharing the same working files. Desktop builds support macOS and Linux; the Windows beta requires Git for Windows. A local browser dashboard is also available.

TensorBoard helps ML developers and researchers inspect TensorFlow experiments through a browser. It shows how training progresses and lets you compare runs when evaluating changes to a model or its hyperparameters.

MLflow helps developers and teams inspect AI applications, compare changes and track model experiments. It brings agent debugging and evaluation into a platform you can run locally or on your own servers.

Deepchecks is a Python toolkit for data scientists and ML teams checking datasets and models before deployment and watching for problems in production. Its testing, CI and monitoring components reuse the same checks across those stages.

Soup is a local LLM fine-tuning tool for developers and teams adapting models to their own datasets. It brings training, evaluation and export into one workflow that runs on your own hardware without requiring a cloud service.

H2O LLM Studio helps teams adapt language models to their own datasets through a browser interface, without writing training code. It's aimed at organizations building models for specific tasks.

ClearML is a self-hostable MLOps platform for researchers and engineering teams managing model development and production. It connects experiment records with datasets, training jobs and model deployment so teams can trace results back to the work that produced them.

Arize Phoenix helps developers investigate failures in AI agents and LLM applications and compare proposed fixes. It connects recorded application activity with evaluations and repeatable experiments.

DVC is a data version control tool for individual data scientists and small machine learning projects. It links datasets and model artifacts to code history so you can recover the inputs behind an experiment and reproduce its results.