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MLOps & LLMOps: Bringing AI to Production

In-depth course on MLOps and LLMOps — the art of bringing AI models from prototype to production safely and effectively. From experiment tracking, CI/CD for ML, to LLM observability, cost optimization, guardrails, and compliance. Highest paying skills in AI.

Introducing the Series

MLOps & LLMOps: Bringing AI to Production is a course for those who want to bridge the gap between "AI running on Jupyter notebook" and "AI serving millions of users in production".

🎯 Hard reality: 87% of ML projects never make it to production. MLOps/LLMOps is the skill set that puts you in the 13% of success — and the highest pay in AI.

What will you learn?

Part 1: MLOps Fundamentals

  • Lesson 1: What is MLOps? ML Lifecycle, Google Maturity Levels
  • Lesson 2: Experiment Tracking: MLflow & Weights & Biases
  • Lesson 3: Data Versioning (DVC) & Feature Store (Feast)

Part 2: Model Management & Deployment

  • Lesson 4: Model Registry, Versioning & Packaging
  • Lesson 5: CI/CD for ML: testing, validation, automated retraining
  • Lesson 6: Infrastructure: Docker, Kubernetes, Cloud ML Platforms

Part 3: LLMOps

  • Lesson 7: 🔥 LLMOps vs MLOps — paradigm shift
  • Lesson 8: Prompt Management & A/B Testing
  • Lesson 9: 🔥 LLM Observability: LangSmith, Langfuse, Arize

Part 4: Production Excellence

  • Lesson 10: Cost Optimization: caching, routing, quantization
  • Lesson 11: Guardrails, Safety & Compliance (EU AI Act)
  • Lesson 12: Capstone: building ML Platform from scratch

Input required

  • Advanced Python (async, decorators, classes, testing)
  • Basic understanding of ML/DL (training, evaluation, inference)
  • Basic Docker (dockerfile, docker-compose)
  • Practical experience with 1+ ML/LLM project is a big advantage

Tools used

Python 3.11+          | Ngôn ngữ chính
MLflow                | Experiment tracking & registry
Weights & Biases      | Advanced experiment tracking
DVC                   | Data version control
Docker / K8s          | Containerization & orchestration
GitHub Actions        | CI/CD pipelines
LangSmith / Langfuse  | LLM observability
FastAPI               | Model serving API
Grafana / Prometheus  | Monitoring dashboards

Compare all AI series

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Difficulty level⭐⭐⭐⭐⭐⭐⭐⭐⭐→⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐

Part 1: MLOps Foundations

Lesson 1: What is MLOps? — ML Lifecycle & Maturity Levels120 minLesson 2: Experiment Tracking — MLflow & Weights & Biases120 minLesson 3: Data Versioning & Feature Store120 min

Part 2: ML Infrastructure

Lesson 4: Model Registry, Versioning & Packaging120 minLesson 5: CI/CD for ML — Testing & Validation Pipelines120 minLesson 6: Infrastructure — Docker, Kubernetes & Cloud ML120 min

Part 3: LLMOps

Lesson 7: LLMOps vs MLOps — Paradigm Shift120 minLesson 8: Prompt Management & A/B Testing120 minLesson 9: LLM Observability — LangSmith, Langfuse & Arize120 min

Part 4: Production & Governance

Lesson 10: Cost Optimization — Caching, Routing & Quantization120 minLesson 11: Guardrails, Safety & Compliance120 minLesson 12: Capstone — Building ML Platform from Scratch120 min