How do I apply Machine Learning Algorithm at Scale in Seeq?

[Sanman Mehta]

Check out the data lab script and video that walks through it to automate data pull->apply ml->push results to workbench in an efficient manner.  Of course you can skin the cat many different ways however this gives a good way to do it in bulk.

Use case details:

Apply ML on Temperature signals across the whole Example Asset Tree on a weekly basis.  For your case, you can build you own asset tree and filter the relevant attributes instead of Temperature and set spy.jobs.schedule frequency to whatever works for you. 

Let me know if there are any unanswered questions in my post or demo.  Happy to update as needed.

Apply ML on Asset Tree Rev0.ipynb

[Emily Johnston]

Thanks for sharing this video, Sanman. As a follow-up, could you advise on best practices for sharing these results once they've been pushed into Seeq?

[Sanman Mehta]

Oh ya these results don't have to be siloed in data-lab python environment.  They can be used as time series signals in workbench as well as organizer topics for any type of visualizations or in further calculations by anyone within the organization.