Design a Lab-Automation Pipeline for a Bangalore Materials Startup
Overview
What this challenge is about.
Design (not build) the full closed-loop lab pipeline: data layer (LIMS plus experiment store), model layer (a surrogate plus an acquisition function such as Expected Improvement), orchestration layer (job queue plus robot interface), and observability layer (model performance, drift, robot health). Specify the active-learning loop: how the model proposes, how a chemist approves, how results flow back. Pick concrete tools (MLflow, Prefect, Weights and Biases, etc.) with reasoning. Write a 90-day rollout plan with weekly milestones for a 5-person eng team plus 4 chemists. Close with a 1-page risk register.
The Brief
What you'll do, and what you'll demonstrate.
Architect and plan the rollout of a closed-loop active-learning lab pipeline for a materials-discovery startup.
Earning criteria — what you'll demonstrate
- Design a multi-component AI/ML system across data, model, orchestration, and observability layers
- Specify an active-learning loop with realistic human-in-the-loop checkpoints
- Make and justify tooling decisions for an MLOps stack
- Plan a rollout that respects a real engineering team's capacity
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
AI Product Manager
Translating a CTO ambition into a 90-day plan a cross-functional team can execute is core AI PM work.
This challenge sharpens
- stakeholder-communication
- active-learning
- mlops-design