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