Databricks MLOps - Using MLFlow Tracking

Databricks MLOps - Using MLFlow Tracking
In this little video series I'll get to the bottom of how you can control the Azure Databricks platform with your DevOps toolbox.

In this part we're trainings and storing our Machine Learning Model while using MLFlow Tracking to log the progress. All these steps are automated by using an Azure DevOps pipeline.

This DevOps for Machine Learning pipeline sets the basis for the deployment with the Azure Machine Learning Service in the upcoming video.

Please take a look at the latest version of the MLOps with Databricks code:
https://github.com/SaschaDittmann/MLO...

If you haven't yet seen the first video in this series, I strongly recommend that you do so:
   • Databricks MLOps - Preparing to use MLflow...  

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🚀 Tools and services used in this video:
Azure Databricks
https://azure.microsoft.com/en-us/ser...

Azure DevOps Services
https://azure.microsoft.com/en-us/ser...

✅ Recommended playlists:
DevOps for Machine Learning (MLOps) on Azure
   • DevOps for Machine Learning (MLOps) on Azure  

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#Databricks #MLOps #MLFlow