MLOps Engineering Bootcamp Certificate
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Bootcamp Mastery Certificate B03
Productionize machine learning from experiment tracking and orchestration through deployment, monitoring, testing, infrastructure-as-code and CI/CD. The official video playlist remains the canonical spine; focused direct videos deepen production practice.
Benchmark: DataTalksClub MLOps Zoomcamp -- DataTalksClub currently makes the complete MLOps Zoomcamp available for self-paced study; provider certification is cohort-dependent and self-paced completion alone does not imply a DataTalksClub certificate. This is a Zuyini Bootcamp Mastery Certificate built around free MLOps Zoomcamp video materials, using the independent proof rubric below.
Core Video Sequence
01 — Why MLOps, lifecycle & production thinking
02 — Canonical sequence: MLflow, pipelines, deployment & monitoring
03 — Deploy a model service with Flask/Docker
04 — Monitoring metrics & drift with Evidently
05 — Integration tests for containerized ML services
06 — Infrastructure as Code foundations
07 — Terraform modules, outputs & reusable infra
08 — End-to-end infrastructure for ride predictions
09 — CI/CD architecture with GitHub Actions
10 — Continuous delivery to production
Build-Along Checkpoints
- Checkpoint 1: Track experiments, parameters, metrics and model artifacts; make a reproducible model-selection decision.
- Checkpoint 2: Convert notebook work into an orchestrated training/scoring pipeline with explicit inputs, outputs and failure behavior.
- Checkpoint 3: Package online or batch inference behind a repeatable deployment path with Docker and environment isolation.
- Checkpoint 4: Instrument model/data health, service behavior and drift; build at least one monitoring dashboard or alert path.
- Checkpoint 5: Add unit + integration tests and a reproducible local test environment for the ML service.
- Checkpoint 6: Provision representative infrastructure with Terraform and automate CI/CD from commit to deployed service.
7-Day Independent Capstone
- Day 1: Choose a real ML use case; define SLOs, retraining triggers, deployment mode, operational risks and evidence plan.
- Day 2: Train candidate models with experiment tracking; register/select the winning artifact with clear lineage.
- Day 3: Build an orchestrated training or batch-scoring pipeline; make runs reproducible and parameterized.
- Day 4: Package the model service or scoring job; containerize and deploy in a production-like environment.
- Day 5: Add model/data/service monitoring with stored metrics, dashboarding and a simulated drift/failure scenario.
- Day 6: Add tests, Terraform and CI; exercise a safe release path and record one rollback or recovery procedure.
- Day 7: Publish architecture, runbook, demo video, operational evidence and a postmortem-style reflection on failure modes.
End-to-End MLOps Capstone
Choose a real ML use case and define SLOs and retraining triggers, track experiments and register a model with clear lineage, build an orchestrated training or batch-scoring pipeline, containerize and deploy the service in a production-like environment, add model/data/service monitoring with a simulated drift or failure scenario, wire tests plus Terraform plus CI/CD with a safe release path, and publish an architecture doc, runbook, demo video and postmortem-style reflection.
Proof Rubric — What Must Be Shown
- LINEAGE: experiments + artifacts + reproducibility
- PIPELINES: orchestration with repeatable runs
- SERVING: online/batch path runs end to end
- MONITORING: metrics + drift + dashboard/alerts
- QUALITY: unit/integration tests and validation
- INFRA: IaC for representative resources
- DELIVERY: CI/CD with release evidence
- OPERATIONS: runbook, rollback, demo + postmortem