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MLOps & LLMOps
CI/CD for models, monitoring, cost control and governance for AI running in production.
Duration
2 weeks
Format
Live online
Commitment
10–12 hrs/week
Level
Practitioner
Next cohort
Starts soon
What you'll learn
Outcomes
Automate training
Build reproducible, CI/CD-driven training pipelines instead of notebook handoffs.
Operate LLMs at scale
Version prompts and models, and manage rollouts with confidence.
Catch drift early
Monitor quality, latency and cost with real alerting, not spreadsheets.
Govern what ships
Apply audit trails and compliance basics fit for regulated environments.
Curriculum
Week-by-week syllabus
- Day 1 — MLOps foundations: from notebook to pipeline
- Day 2 — CI/CD for models & reproducible training
- Day 3 — Feature stores & data versioning
- Day 4 — Model registries & rollout strategies
- Day 5 — Lab: build an automated training pipeline
- Day 6 — LLMOps: prompts, evals & versioning
- Day 7 — Monitoring, drift detection & alerting
- Day 8 — Cost control & inference optimization
- Day 9 — Governance, audit trails & compliance basics
- Day 10 — Capstone: deploy a monitored inference pipeline
Prerequisites
- Working knowledge of Python
- Familiarity with basic ML or software deployment concepts
- Some exposure to cloud infrastructure is helpful
Choose your domain
Projects and datasets adapt to where you work:
FinanceHealthcareRetailGovernmentManufacturingGeneral
ML
Lead instructor
A practitioner who ships agents in production
Your cohort is led by a senior AI solution architect with real-world experience building multi-tenant AI platforms and autonomous systems — every project is reviewed personally.
Ready to build production AI skills?
Join the next 2-week cohort, or bring this program in-house for your team.