> ## Documentation Index
> Fetch the complete documentation index at: https://docs.domino.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Develop models

This article introduces training machine learning models in Domino. See the links below to learn about Domino’s benefits and model development concepts.

## Train with your favorite libraries and IDEs

Domino is a flexible platform that’s designed to work with your favorite tools:

* [Develop and train models](/cloud/platform-capabilities/features/development/develop-train-models) with open-source libraries and your favorite IDEs.

* [Capture your GenAI runs](/cloud/platform-capabilities/features/genai/agent-development-lifecycle) with tracing to see how a GenAI experiment runs step by step.

* Implement MLflow to [track and monitor experiments](/cloud/platform-capabilities/features/development/track-monitor) in R and Python.

* [Scale distributed workloads](/cloud/platform-capabilities/features/development/distributed-training) with Dask, Ray, MPI, and Spark.

* [Tune hyperparameters at scale with Ray Tune](/cloud/platform-capabilities/features/development/ray-tune) and other open source libraries.

## Manage experiments and collaborate with others

Domino helps you manage experiments and models across your organization:

* [Track and monitor experiments](/cloud/platform-capabilities/features/development/track-monitor) to see logs, outputs, and more.

* [Schedule jobs](/cloud/platform-capabilities/core-concepts/jobs/schedule-a-job) for recurring processes.

* [Develop projects with Git](/cloud/platform-capabilities/core-concepts/projects/manage-git-projects) version control.

## Next steps

After you understand how to train models in Domino, learn how to [Deploy Models and Apps](/cloud/platform-capabilities/core-concepts/products/models).