docs: Credit-risk-end-to-end example #4630
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examples/credit-risk-end-to-end/02_Deploying_the_Feature_Store.ipynb
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dmartinol
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@franciscojavierarceo I've adjusted this PR based on the feedback given. I believe it's ready to merge. |
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Signed-off-by: dandawg <12484302+dandawg@users.noreply.github.com>
Signed-off-by: dandawg <12484302+dandawg@users.noreply.github.com>
Signed-off-by: dandawg <12484302+dandawg@users.noreply.github.com>
Signed-off-by: dandawg <12484302+dandawg@users.noreply.github.com>
Signed-off-by: dandawg <12484302+dandawg@users.noreply.github.com>
Signed-off-by: dandawg <12484302+dandawg@users.noreply.github.com>
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What this PR does / why we need it:
This PR presents a clear step-by-step guide aimed at data science teams who need a basic hands-on introduction to Feast. I use the AI modeling workflow to demonstrate Feast in-context. In this example, the data doesn't randomly appear with
feast init, and it's not randomly generated (except for timestamps and IDs). The example data comes from a popular OpenML dataset, and the reader progresses through data preparation, model training, and model serving. The example walks through the basic steps of the AI modeling process, and Feast solutions are brought in where they naturally provide value.As people of different roles (data scientists, data engineers, DevOps, AppDevs, etc.) need introductions to Feast and how it fits in the AI/ML flow, I've kept the example approachable, so that someone who doesn't know all of the technical disciplines can follow along and get an idea of the full picture.
Which issue(s) this PR fixes:
The quickstart documentation and other examples are great for describing Feast functionality, but they use randomly generated data with no real patterns, and they are not as clear on where things fit in the process of doing AI/ML.
Misc