Learn to Use Databricks for the Full ML Lifecycle

Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
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[@CoopmanGreg]

Watched hundreds of Databrick videos. This is the best by far. Super Clear. Thanks!

[@AprenderDados]

Does anyone know where to get the notebooks and data samples for this?

[@Raminber]

It would be great to get access to the notebooks and dataset!

[@hari2002]

Detailed MLOps life cycle Demo, thanks

[@goulartto5616]

Very nice demo! Thanks Rafi Kurlansik!

[@_aniket_gupta]

Super helpful! Thanks a lot.

[@sharedpollard4407]

It find it hard to follow how the developer knows to put some of this MLflow code into the notebook when not using AutoML. If this notebook demo and data are available that would be great!

[@panasrat457]

Great work! Thank you.

[@tarshmidha5879]

what's the link to data preparation video that was mentioned and worked on by data engineering person?

[@Dragonblade7000]

So valuable, thank you

[@toofrellik]

can we access these notebooks anywhere?

[@krishj8011]

great tutorial

[@samuelmathias794]

How can I get the code to this tutorial

[@DionisiosML]

great video

[@hussamcheema]

Helpful, but it was a little fast paced.

[@toulasantha]

Guys please remove adds