Azure Data Scientist Associate

 The last week only, I renewed my Azure Data Scientist Associate certification. Before the first re-attempt, I promised myself to spend some time on preparation. Was it worth it?




Of course! I love it. The preparation for certification forces me to take a closer look at the aspects that are out of my daily interest. In my current project, the limited part of Azure ML was in use - the Data Drift, which is a small piece of the bigger piece. During my study, I've walked through the whole journey once again, starting from data preparation, and ending up with the model deployment on the cloud.

This revealed the mystery, of why I wasn't able to convince my client since the last year to go deeper into that platform, despite the fact, that the project itself is data science-driven and already includes all the juicy parties,  like model training (PKL) and data/feature preparation. 


The answer was simple. Following the bible - "And how can they hear about someone if no one is proclaiming him?".  I was so proud last year of my certification achievement that I've simply forgotten to evangelize my colleagues with the updated ML religion doctrine.

To not fail this time, I've started with taking existing python code that was used to train the model already deployed on production. Then, I introduced an AML experiment to wrap it up around the existing code. The next step was to register the data-set in the cloud and extract the data from the developer (data scientist) local machine, ... and so on. Those baby steps let me move from "known to unknown", providing my colleagues with an insight into the additional values that came up with each code change... 

The raised questions that came during the session (about the regressors,  Grid Search evaluators or hipper-parameters) might be not obvious to everyone. I wouldn't be able to come up with any of my answers without my exam preparation (brain refurnishing).

The team is so excited about the new possibilities and has already built a plan for the more trilling ML cases . ..It's time to crunch the data and build the s shiny Battlestar PKL model ;)

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