Job description to validated JSON
Turn messy job descriptions into evidence-backed records, with missing-field handling and repeatable tests.
Go beyond a tutorial copy. Build a useful system, measure where it fails and turn your decisions into portfolio evidence.
Find your next project ↓20 of 20 projects · Estimates are focused build hours, not guaranteed completion times.
Turn messy job descriptions into evidence-backed records, with missing-field handling and repeatable tests.
Build a forecast API, compare it with a seasonal baseline and prove that future information never enters training.
Draft short reports from a controlled evidence pack, checking citations and refusing unsupported conclusions.
Analyse a fictional onboarding experiment with uncertainty, guardrail metrics and an honest decision memo.
Compare two model configurations on factual extraction, separating accuracy, malformed output, warm-up time and speed.
Build grounded search with access checks, citation evaluation and explicit refusal when evidence is missing.
Build a bounded tool workflow with approval, idempotency and recovery, using a sandbox instead of real customer actions.
Ingest noisy events into a versioned dataset with contracts, deduplication, quarantine and safe reprocessing.
Promote a tiny model only after quality and operational checks pass, then demonstrate rollback when a canary regresses.
Reject unusable product photos, compare rules with an optional small classifier and test lighting-related failures.
Recommend useful learning resources from skills and goals, with cold-start handling and transparent ranking evaluation.
Answer questions over a fictional database with validated SQL, read-only permissions and explicit ambiguity handling.
Search a permitted image catalogue using descriptions and compare cross-modal retrieval with a keyword baseline.
Compare transcripts with human references, separating word errors, critical terms, noise and processing time.
Control demo slides with explicit hand gestures, cooldowns and a keyboard fallback, while keeping camera frames local.
Detect and mask selected personal information in fictional text, with span-level tests and a human-review queue.
Detect unusual synthetic service metrics and evaluate alert precision, missed incidents and detection delay.
Compare a LoRA-adapted Gemma classifier with a prompt-only baseline, using held-out tickets and explicit resource accounting.
Find differently worded duplicate records without merging distinct items that happen to share a topic.
Classify permitted everyday sounds and investigate how background noise, chunking and thresholds change results.
Pick a scope you can complete, create a simple baseline, freeze a test set and write down what fails. A clear README, reproducible evaluation and thoughtful trade-offs are more useful than a long list of unfinished demos.
These are educational blueprints, not downloadable finished applications or hiring guarantees. Google documentation and GitHub references are labeled separately. API usage and optional cloud infrastructure may incur charges.