Excellent coverage of the capabilities available in Spark/Databricks generally and MLflow in particular.

Excellent workshop! thanks for sharing!

Is there a repository on which we can find this code? Excellent content!

very well explained and excellent content. got to know about hyperopt & shap packages and their usefulness. Super like

Where can I get the notebook file？

Great content! I may have missed it: Let's say I have a model that requires my features to be transformed before handing them over to the predictor. Is it possible to include that in the mlfow model container? Or is this something that is required to happen, e.g., on the client if I'm serving using a REST API? Thanks.

Please add a repository location for the examples in this video.

Could you please share the notebooks? great content

Is there any api code snippets to enable model serving? we want to automate enable model serving. Please help. thank

Great content! Is the notebook available?

Thanks for the content! Question: At about 22 minute 4 sec mark, you say "when we log this..." and then click on something to get a different view. What do you click on there?

Good video!

Excellent Content !! Could you please share the notebooks and data source? Thanks !!

very good stuff

please share the git hub code or notebook if any

It is very unfortunate to see the whole ML/AI world learned nothing from Statistical modeling which has been happening for the last 50-60 yrs. Maybe this is what happens when money becomes too cheap, and investment becomes too easy to achieve. This video is a perfect example of OVERENGINEERING!! The dev cost is so high it is practically impossible to earn any money by selling it and becoming cash positive. The bubble is waiting to kill the industry BADly.