Experiment Tracking and Data Lineage

Description

These projects typically track the history of machine learning experiments, capturing input parameters and results. They may also track the steps in a data pipeline and how a final data set was derived from source data.

Projects

5

Lines Committed vs. Age Chart (click to view)

Lines Committed vs. Age Chart (click to view)

Projects

Project

Size Score

Trend Score

Byline

Data Workspaces

1.75

3.0

Easy management of source data, intermediate data, and results for data science projects.

Flambe

1.5

1.0

Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference.

MLflow

6.75

4.75

An open source platform for the machine learning lifecycle

PyCaret

7.5

7.0

An open-source, low-code machine learning library in Python.

rubicon-ml

1.5

8.0

rubicon-ml is a data science tool that captures and stores model training and execution information, like parameters and outcomes, in a repeatable and searchable way.