Amazon controlled MLflow Tracking Servers

A popular open-source tool called MLflow is essential for assisting machine learning (ML) teams in handling the full ML lifecycle

With MLflow, data scientists and machine learning developers may monitor several iterations of model training as runs within experiments

Using the SageMaker Studio UI, you can quickly establish an MLflow Tracking Server

An additional option for finer-grained security customisation is to use the AWS Command Line Interface

This ensures thorough recording and management of your machine learning experiments

The MLflow artefact store component offers a place to store all of the artefacts produced by machine learning experiments, including datasets, trained models

Monitor MLflow experiments in managed IDEs in SageMaker Studio, local IDEs, training jobs, processing tasks, and pipelines