Full-Stack Deep Learning / Production AI Bootcamp Certificate
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Bootcamp Mastery Certificate B07
Primary spine: Full Stack Deep Learning 2022. This path focuses on the full production lifecycle around deep learning: problem selection, data, infrastructure, experiments, testing, deployment, monitoring, continual learning, teams and responsible delivery.
Benchmark: Full Stack Deep Learning states that its 2022 lecture and lab material is free. Zuyini uses those public videos as the learning spine and applies its own independent build-and-proof completion rubric; this does not imply a provider-issued Full Stack Deep Learning certificate.
Core Video Sequence
01 — Complete production ML course sequence
02 — Scope ML products + define success
03 — Infrastructure, compute + experiment tooling
04 — Testing, regression + troubleshooting
05 — Data sourcing, labeling + versioning
06 — Model serving, scaling + rollouts
07 — Monitoring, feedback + continual learning
08 — Foundation models + adaptation patterns
09 — ML teams, product design + project management
10 — Responsible deployment + ethical tradeoffs
Build-Along Checkpoints
- Checkpoint 1: Define a high-value ML use case, user workflow, baseline, success metric, failure cost and feasibility/ethics gate.
- Checkpoint 2: Create a versioned dataset and training pipeline; document provenance, labeling assumptions, splits and leakage controls.
- Checkpoint 3: Track experiments and artifacts; compare at least three controlled variants and preserve reproducible environment metadata.
- Checkpoint 4: Add data/model/unit/integration tests plus one regression gate; intentionally break a case and show the test catching it.
- Checkpoint 5: Deploy inference behind an API or web application; measure latency, throughput, error rate and one scale/cost tradeoff.
- Checkpoint 6: Instrument prediction quality and system health, capture user/ground-truth feedback and run one continual-improvement cycle.
7-Day Independent Capstone
- Day 1: Frame the product: users, workflow, baseline, target metric, constraints, risk register and architecture.
- Day 2: Build/version the data and training pipeline; create reproducible experiments with tracked artifacts and decisions.
- Day 3: Add software/data/model tests, regression thresholds and one performance troubleshooting exercise.
- Day 4: Deploy a minimum viable service with API/UI boundaries, auth where relevant and measured latency/throughput.
- Day 5: Add monitoring for system and model behavior; define drift/quality alerts and collect feedback for retraining.
- Day 6: Trigger a degradation scenario, investigate it, roll back or remediate, then run a controlled improvement/retraining cycle.
- Day 7: Publish repo, model card, architecture, experiment report, test evidence, dashboard, runbook and product-impact memo.
Production AI - Build, Deploy, Monitor & Improve
Turn a trained model into a production ML product. Completion requires a credible product target, reproducible experiments, testable data/model pipelines, a deployed service, telemetry, a feedback loop and evidence that the system can recover and improve after launch.
Proof Rubric — What Must Be Shown
- PRODUCT: Problem fit, baseline, success + risk
- DATA: Provenance, labels, splits + versioning
- EXPERIMENTS: Tracked runs + reproducible decisions
- TESTING: Data/model/software regression gates
- DEPLOYMENT: API/UI, scale + latency evidence
- MONITORING: Quality, system health + alerts
- CONTINUAL: Feedback, retraining + recovery
- RESPONSIBILITY: Ethics, limitations + user impact