Learn AIWhy does AI need computation?
Understand the tradeoffs between model size, data, and computation.
What you’ll explore
- Model scale
- Data efficiency
- Compute budgets
Your lessons
0 / 8 completeSpend a limited compute budget
Balance model size and training data, distinguish active weights from stored weights, and measure a small browser computation.
Package a model as a component
Export and reload a real local model bundle; check named inputs, fitted preprocessing, dependency hashes and output meanings.
Measure and optimize inference
Profile actual local inference, separate cold and warm traffic, and inspect precision error before choosing an optimization.
Version data and experiments
Reconstruct a result from local data, code, configuration and artifact records; locate changed preprocessing.
Deploy with a reversible plan
Compare local and service boundaries, inspect shadow and staged traffic, and restore stable routing without erasing history.
Test the complete system
Build a test suite and expose what unit, numerical, data, integration and behavior checks catch or miss.
Monitor what changed
Replay changing traffic, request delayed labels and allocate limited alert reviews.
Respond to an incident
Trace a release regression, contain its impact and verify recovery without rewriting the incident history.
Build and evaluate a classifier
Put the four modules into practice: train, compare, test, and explain.