Introduction to Vertex AI Feature Store

Feature engineering involves transforming raw data into high quality input signals for ML models, but what if there was a simpler way? In this episode of AI Simplified, we’ll identify the challenges with feature engineering, and show how Vertex Feature Store helps solve them. Watch for a quick demo and see if Feature Store is right for you!

Chapters:
0:00 - Intro
1:40 - Demo
6:27 - Summary

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#AISimplified

product: Cloud - AI and Machine Learning - AI building blocks; fullname: Priyanka Vergadia; re_ty: Publish;

[syamkumars]

Appreciate more hands on sessions video also.

[moraholguin]

Hi,

I have a Google Colab notebook with some functions (Python) that been used to calculate the features for a model.

The functions use as inputs data from an API.

The question is if I can or should calculate the features inside a Features Store and feed the results to the Model?

Or in which Instance do I need to make the calculations and then feed the results into the model?

[sylwesterguzek]

Hi Priyanka,
I have created one feature from web UI, I haven't proceeded with any furter steps, I left it as it was and after a month I received from google invoice for 740$ for using it... I believe there is some underdevelopment from google side on that particular functionality. It is not even possible to delete this feature. Can you help me out with that ?

[ray]

It can be used both in online and offline serving.

[DaGo-AI]

Hello Priyanka,

Thanks for creating this content. It's very helpful. When will there be more content? (Vertex Explainable AI, for example)

Best regards,

[shanthinagasubramanian]

Hi Priyanka, not clear on the difference between feature store and dataset. Could you make a video explaining that.