🤖 AI/ML Workflows • All Levels • 20-30 min

Build vs Buy vs API Decision Workbench — PM Demo 03

Build vs Buy vs API Decision Workbench — PM Demo 03 About Demo for AI/ML Workflows: browser-based learning activity covering ai workflow design, model-assisted decisions, human oversight. Students explore build, buy, and api options across cost, speed, control, and risk with no cloud or API keys required.

About Demo Browser Browser SLM / local AI Attribution: vinallcontact@gmail.com

What this demo is about

Concept first, interaction second

**Repository:** [KateelLearningDemosToStudents](https://github.com/VinayaSharada/KateelLearningDemosToStudents) **Author:** Professor Vinaya Sathyanarayana **Section:** `🤖 Browser-AI-Demos/PM-Product-Demos/3-build-buy-api-workbench/`.

Learning objectives

  • Explain the main ai/ml decision that Build vs Buy vs API Decision Workbench — PM Demo 03 is designed to support.
  • Change input assumptions and predict how the output should respond before running the demo.
  • Interpret the result in plain language, not just as a number, chart, or AI recommendation.
  • State one limitation, risk, or governance consideration before using the result in a real decision.

Run modes

  • Supported modes: Browser
  • Demo type: Interactive browser demo
  • Primary launch surface: index.html

Before you start

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

This helps set classroom expectations before students click into the live experience.

Business or domain context

Why this demo matters

Students should connect the demo to a real decision, not treat it as a standalone screen.

Core context

Look for build, buy, and API options across cost, speed, control, and risk.

Observe how the best choice changes when time-to-market or compliance matters more.

Concepts covered

AI workflow design
Model-assisted decisions
Human oversight
Prompt and output evaluation

What students should note

Note that platform decisions should be revisited as strategy and constraints evolve.

How to use the demo

Recommended classroom flow

List of steps

  • Choose the run mode that fits the class: Browser.
  • Review the default assumptions before changing anything.
  • Change one or two inputs, then use `Run the main action`.
  • Read the output first, then compare any supporting metrics, charts, or AI text.
  • Capture one insight, one limitation, and one action recommendation.

Input variables explained

  • Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
  • Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Decision buttons explained

  • Use the main run or simulate action to compute the scenario after inputs are set.
  • Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Outputs and interpretation

How to read the result

Outputs explained

  • Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
  • Students should explain whether the output is descriptive, predictive, simulated, or recommended.

What to notice

  • Look for build, buy, and API options across cost, speed, control, and risk
  • Observe how the best choice changes when time-to-market or compliance matters more
  • Note that platform decisions should be revisited as strategy and constraints evolve

Discussion and reflection

  • What business or technical decision would you make differently after using Build vs Buy vs API Decision Workbench — PM Demo 03?
  • If you changed one assumption and ran `main action`, which output moved the most and why?
  • What would you still want to validate with real data, policy, or expert review before acting on the result?

Faculty guide

Prompt for discussion or assessment

Have students toggle AI on/off and document what changed in the recommendation, not just the final answer.

Suggested interpretation prompt: Ask learners to explain how the output changed, what assumption caused it, and what real-world check they would do next.

Feedback

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Attribution & reuse

Created by Professor Vinaya Sathyanarayana as part of KateelLearningDemosToStudents. Please retain attribution and notify usage at vinallcontact@gmail.com.