Demos break quietly once real traffic hits them — learn the versioning, testing, monitoring, and rollback habits that keep them reliable.
Your model worked in the notebook. Then a teammate asks which data trained it, a one-word prompt tweak silently drops answer quality, and nobody notices until a customer complains. This course teaches the operating habits that prevent all three: MLOps (machine learning operations) for trained models, and LLMOps (large language model operations) for apps where you never train anything and the real artifacts are prompts, retrieval configs, and agents. In Python, you'll track experiments with MLflow so any result can be rebuilt, version datasets with DVC (Data Version Control), publish models and prompts to a registry with full lineage, and wire up CI/CD (continuous integration and delivery — automation that runs on every pull request) that fails the build on a quality regression. Then you'll ship with canary and blue/green rollouts plus automated rollback, build an LLM eval pack with a golden dataset and a calibrated judge, instrument tracing, cost, and drift alerts, and wrap the whole thing in a guardrail layer you red-team yourself. Every module ends in a project, and the capstone takes one real feature all the way around the loop.
Built by Lakshya Kumar
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Your best run lived in a notebook you overwrote — log params, metrics, seeds, and prompts so any result can be rebuilt months later.
Training data shifted and nobody noticed — snapshot every dataset, validate each batch, and catch train/serve skew before it reaches users.
When one prediction goes wrong you need the exact version, its data, and who approved it — a registry answers that in seconds, not days.
A model can pass every unit test and still be worse — add data, behavioral, and regression gates so quality drops never reach main.
Send the new model to 1% of traffic, watch the metrics, and let an automated rollback pull it before most users ever see it.
One prompt tweak can quietly wreck answers — build a golden set, a calibrated judge, and a gate that fails the build on any regression.
When quality dips at 2am, traces and drift alerts tell you which prompt, model, or input moved — instead of guessing from one screenshot.
Users will paste secrets and try to hijack your prompt — filter input, redact personal data, validate output, and red-team the whole layer.
Today's production failures should become tomorrow's test cases — wire feedback, retrain triggers, cost caps, and runbooks into one loop.
Complete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Take one real ML model or LLM feature all the way around the loop: register it (with a model card, provenance, and lineage), gate it with an automated eval suite in CI, deploy it via canary behind feature flags with automated rollback, wrap it in an observability + guardrail layer, and write the runbooks. Submit the repo, a CI run showing the eval gate, a deployment + rollback demo, an observability dashboard, and the runbooks.
I'm taking an "MLOps & LLMOps: The Production Lifecycle" course — the operational discipline behind reliable ML and LLM systems. It covers experiment tracking (MLflow/W&B), data & feature pipelines (DVC/Airflow), model & prompt registries with lineage, CI/CD with eval gates (GitHub Actions), deployment patterns (shadow/canary/blue-green/A-B), automated LLM evals (golden sets, LLM-as-judge, regression gates), production observability (Langfuse tracing, drift detection), guardrails & safety (injection defense, PII, moderation), and closing the continuous loop. Python-first, bridging classic MLOps to 2026 LLMOps. Here's my context: 1. What I'm building/operating: [describe the ML model or LLM feature/product] 2. Do I train/fine-tune anything, or is every "model" a hosted API call? [train / fine-tune / pure API / mix] 3. My current maturity: [level 0 notebook + manual deploy / level 1 some automation / level 2 full CI-CD] 4. Where it hurts most: [can't reproduce results / no evals / regressions ship silently / drift / cost / safety / slow iteration] Given that, answer: - Which module should I prioritize first and why, given my maturity level? - Which is my single highest-leverage gap (tracking / data versioning / registry / CI eval gate / observability / guardrails / the loop)? - Name 3 concrete changes I could make this week, and how I'd measure that each one helped. - Name 1 thing this course won't fix so I have the right expectations.
Build a reusable eval pack another team could drop onto their LLM app: a versioned golden dataset, reference-based + reference-free checks, a calibrated LLM-as-judge with bias controls, a resilient runner (concurrency caps, retries, checkpointing, dead-letter) that survives rate limits, and a CI regression gate. Submit it as a small package or repo with docs and a sample run.
Instrument a real LLM app end to end: nested-span tracing, responsible logging (sampling + PII redaction), error-type classification, data/concept/output drift detection, online quality signals, and a dashboard with symptom-level alerts. Submit the live dashboard, a debugged trace from a failing request, and proof that a drift or cost spike fires the right alert.
Wrap an LLM feature in a complete guardrail layer (input sanitization, PII redaction, prompt-injection defense, content moderation, schema validation/repair, fallbacks + human-in-the-loop) and red-team it with attack tests for injections, PII leaks, malformed output, and policy violations. Submit the implementation and the red-team suite proving the guardrails hold.
Close the lifecycle loop for a real system: production feedback feeds the golden/training set, an automated trigger drives iteration, the eval gate guards quality, a gated deploy ships behind guardrails, and observability feeds the next turn — plus a unified deployment manifest, cost governance with a spend cap, and a tested runbook. Submit a demo of one full automated loop turn and the manifest/runbook artifacts.
The tracking + registry tool used across Modules 2, 4, and 5. Keep it open while you build.