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Experiment tracking server focused on scalability

FastTrackML is a self-hosted machine-learning experiment-tracking server that receives run data through MLflow and presents experiment results in a web UI. It offers a Classic interface modeled on MLflow's tracking-server experience and an Aim-inspired interface for reviewing runs.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Experiment tracking server focused on scalability — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use FastTrackML when you want to host a tracking server and send it MLflow experiment data for dashboard-based review.

Who it is for

  • Machine-learning practitioners who want to record and inspect experiment parameters and metrics.
  • Teams looking for a self-hosted experiment-tracking server with MLflow-based logging.

Documented uses

  • Run an experiment-tracking server for machine-learning projects.
  • Log experiment parameters and metrics through the MLflow Python package.
  • Review run information and metrics in a web UI.
Before you choose

Requirements & limits

  • The documented installation paths are pip, platform-specific scripts for Linux/macOS and Windows, and Docker.
  • The pip-based installation and logging example require Python 3; the container route requires Docker to be running.
  • The documented sample sends data to a local server at port 5000 and displays the run through the /aim/ UI.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start FastTrackML and log a sample experiment

Set up a local tracking server and send it a sample MLflow run for review in the web UI.

Before you start

  • Python 3 for the pip installation and experiment-logging example, or Docker running for the container option.
  • The MLflow Python package, installed as mlflow-skinny for the documented logging example.

Steps

  1. Install FastTrackML with pip, the documented operating-system install script, or the Docker image.

  2. Start the server with fml server, or run the documented Docker container on port 5000.

  3. Set the MLflow tracking URI to http://localhost:5000 in a Python script, select an experiment, and log a parameter and metric within a run.

  4. Open the local server's /aim/ page to review the run information and metrics.

Expected outcome

A sample experiment run is recorded on the local FastTrackML server and can be inspected in the Aim UI.

Guide limitations

  • The instructions describe a local quickstart; they do not provide production deployment or operations guidance.
Documentation 1 ↗
Evidence and transparency

Sources & review

fasttrackml.io ↗
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Sources checked
12 October 2026
Original website title
Experiment tracking server focused on scalability | FastTrackML · Official homepage title · retained in its source language
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