Machine Learning Feature Store Panel Discussion // MLOps Coffee Sessions #26

Coffee Sessions #26 with Vishnu Rachakonda of Tesseract Health, Daniel Galinkin of iFood, Matias Dominguez of Rappi & Simarpal Khaira of Intuit, Feature Store Master Class.

//Bio
Vishnu Rachakonda
Machine Learning Engineer at Tesseract Health. Coffee sessions co-host but this time his role is one of the all-stars guest speakers.

Daniel Galinkin
One of the co-founders of Hekima, one of the first companies in Brazil to work with big data and data science, with over 10 years of experience in the field. At Hekima, Daniel was amongst the people responsible for dealing with infrastructure and scalability challenges. After iFood acquired Hekima, he became the ML Platform Tech Lead for iFood.

Matias Dominguez  
A 29-year-old living in Buenos Aires, past 4.5 years working on fraud prevention.  Previously at MercadoLibre and other random smaller consulting shops.

Simarpal Khaira
Simarpal is the product manager driving product strategy for Feature Management and Machine Learning tools at Intuit. Prior to Intuit, he was at Ayasdi, a machine learning startup, leading product efforts for machine learning solutions in the financial services space. Before that, he worked at Adobe as a product manager for Audience Manager, a data management platform for digital marketing.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Daniel on LinkedIn: https://www.linkedin.com/in/danielgalinkin/
Connect with Matias on LinkedIn: https://www.linkedin.com/in/mndominguez/
Connect with Simarpal on LinkedIn: https://www.linkedin.com/in/simarpal-khaira-6318959/ 

Timestamps:
[00:00] Introduction to guest speakers.
[00:33] Vishnu Rachakonda Background
[01:00] Guest speakers' Background
[03:13] Are Feature Stores for everyone?
[04:02] Guest speakers' Feature Store background
[17:09] How do you go about gathering requirements for a Feature Store and customize it?
[17:34] Guest speakers' process for Feature Store
[31:14] What solution are we actually trying to build?
[36:42] How do you ensure consistency in your transformation logic and in your process generating features?
[43:39] In terms of versioning that transformation logic and knowledge that goes into creating Feature Stores and allowing them to be reusable and consistent, how are you going to grapple with that?
[48:06] How do you bake in best practices into the services that you offer?
[49:34] "It's too possible for you to do something wrong. You have to specify that wrong thing. That makes it harder to do that wrong thing." Daniel
[51:54] "It starts with changing the mindset. Making people getting the habit of what is the value here. Then you are producing features for consumers because tomorrow you could become a consumer. Write it in a way as you want to consume somebody's feature." Simar
[56:51] "As part of that process, it should come with everyone's best practices to actually improve all features" Matias

Episode source