This video talks about Azure Machine Learning Pipelines, the end-to-end job orchestrator optimized for machine learning workloads. With Azure ML Pipelines, all the steps involved in the data scientist’s lifecycle can be stitched together in a single pipeline improving inner-loop agility, collaboration, and reuse of data and code, while maintaining high reliability.
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confused? me too. Really a pipeline is an ETL workflow defined in Azure DataFactory. These guys said everything but that …but what do i know… i'm just a noob.
Are we supposed to use either Azure Machine Learning Services or MLFlow in Azure Databricks individually for supervised learning or is there a use case where both AML and MLFlow can be used together ?
I mean , should I even bother learning Mlflow if I am able to use the AML service to perform my data science experiments ?
I want to trigger ML pipelines thru ADF. I want to use secrets under Azure key vault instead of manual entries like Subscription ID, resource groups, workspace name, Tentant and Service Principal IDs. Can you please guide me with the procedure?
No running example??