Find your way to MLflow without confusion
<p><strong>MLflow</strong> is probably the most popular tool for <strong>model registry</strong> and <strong>experiment tracking</strong> out there. MLFlow is open source and integrates with a lot of platforms and tools.</p>
<p>Due to its extensive support and a lot of options, getting started with MLflow may feel overwhelming. In this article, we will get back to the basics, and will review <strong>3</strong> <strong>most important</strong>, in my opinion, <strong>classes in MLFlow:</strong></p>
<ul>
<li><strong>mlflow.entities.Experiment</strong></li>
<li><strong>mlflow.entities.Run</strong></li>
<li><strong>mlflow.entities.model_registry.ModelVersion</strong></li>
</ul>
<p>We will see how those entities get created, how you can retrieve them, and how they change based on different input parameters. In this article, the Databricks version of MLflow is used, so it contains some Databricks-specific information. However, the idea is generalizable to any MLflow instance.</p>
<p><a href="https://medium.com/marvelous-mlops/find-your-way-to-mlflow-without-confusion-d86bc710fc73"><strong>Website</strong></a></p>