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Minimal product

As explained in Machine Learning System Design

The smallest product version that delivers enough functionality to test or serve a core use case.

A minimal product is the simplest working version that delivers early value while testing whether a core product hypothesis is sound. In ML systems, it may be a baseline model, feature set, or other functional system component rather than a sophisticated model. section 8.1

Use the minimal product to:

  • Reduce market, use-case, economic, time, cost, and implementation risk before investing in complexity. section 8.1
  • Test the central product hypothesis, gather early feedback, and fail fast when the overall idea is wrong. section 8.1
  • Deliver customer value early, then update the system stage by stage. section 8.1
  • Act as a smoke test by checking that system components work and produce the required output format. section 8.1

How Machine Learning System Design covers it

How deep it goes
Defines goals and smoke-test behavior, but stops short of specifying a particular model, feature set, or implementation.
Examples
Uses a simple predictor that produces the required format as a smoke test for an ML system.
What it assumes
Assumes a product hypothesis and intended system output, while treating model sophistication as unnecessary at the start.
What you can do after
Evaluate a product hypothesis, collect early feedback, and verify that system components work before building complexity.
How it connects
Connects minimum viable product, baseline, smoke tests, fail-fast development, user value, and product hypotheses.
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