Module 7

Frontend & ML Product Thinking

Put the model in a non-ML human's hands: a minimal dashboard that calls the API and shows a prediction someone can act on — without lying about confidence.

Sarah Floris Instructor Sarah Floris · Lead ML Engineer Coming soon

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Wrap the model in a product people can use

A serving endpoint with structured responses

You arrive with a deployed endpoint that returns status codes and structured responses including a model version, refuses bad input, and distinguishes readiness from liveness. This module builds the frontend that consumes it.

serving endpoint structured responses

5 chapters

5 lessons

01 What This Module Covers

The arc of Frontend & ML Product Thinking: a dashboard that calls the serving endpoint and shows a prediction a non-ML human can act on, a page that degrades honestly when the model is down, and the product judgment that turns a raw probability into a decision.

02 Get a Working Dashboard Up

Build the smallest real page that calls the M6 /predict endpoint and renders a prediction a non-ML human can read: input, fetch, render, end to end.

03 Make It Degrade Gracefully

The dashboard works when the API answers. Harden the case it does not — deploying, overloaded, or down — so the page tells the truth instead of blanking out or showing a stale guess.

04 A Prediction Is Not a Decision

A raw probability misleads a non-ML human, the threshold that turns it into a yes/no is a business decision the frontend owns, and an API key in client JS is already public.

05 What You Built in This Module

A recap of Frontend & ML Product Thinking: a dashboard that calls the endpoint without the async bug, degrades honestly when the model is down, and reframes a raw probability into a decision a person can act on — and where the system goes next.

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