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MLOps Tutorial

Learn MLOps (Machine Learning Operations) from the first idea to running models in production, step by step, in simple words. Every idea is shown with animations and real code, real terminals and real tools: DVC, MLflow, pytest, Docker, FastAPI, Kubernetes, drift detection, retraining, LLMOps, and a complete hands-on capstone project.

📚 80 lessons▶ 3:04:27+ of video💻 74 code examples🖼 119 screenshots
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What you will learn

  • Why models fail silently in production, and the ML life cycle
  • Versioning data with DVC, validating data, tracking experiments with MLflow
  • Reproducibility, features, leakage, feature stores and pipelines
  • Pipelines, orchestration, hyperparameter tuning, the model registry and quality gates
  • Testing data, models and behaviour; model formats and ONNX
  • Docker, FastAPI serving, batch vs online, deployment strategies, Kubernetes and CI/CD
  • Monitoring, drift detection (KS test, PSI), delayed labels and retraining
  • Responsible ML: fairness, explainability, governance, security, privacy
  • LLMOps: prompts as code, evaluation, guardrails, RAG, cost and latency
  • Optimisation, GPUs, inference servers, edge ML, FinOps and team practices
  • Reliability: SLOs, alerts, dashboards, incidents and disaster recovery
  • Case studies, a full capstone project, quizzes, interview preparation and a cheat sheet

Course contents

Every lesson has its own page with the explanation, code you can copy, the real output, screenshots from the video, and a link that jumps straight to that moment in the video.

3. Experiments and Reproducibility

  1. Experiment Tracking14:50
  2. Reproducibility17:48
  3. Features and Pipelines20:23

4. Pipelines and Tuning

  1. Training Pipelines23:12
  2. Orchestration25:37
  3. Hyperparameter Tuning28:08

5. Registry, Testing, Formats

  1. The Model Registry30:27
  2. Testing ML Systems32:45
  3. Model Formats35:33

6. Containers and Serving

  1. Containers for ML37:57
  2. Serving with FastAPI40:43
  3. Serving Patterns43:15

7. Deployment and Delivery

  1. Deployment Strategies45:39
  2. Models on Kubernetes48:26
  3. CI/CD for ML51:02

8. Monitoring and Drift

  1. What to Monitor53:38
  2. Detecting Drift56:00
  3. Quality and Retraining58:28

9. Responsible ML

  1. Fairness1:01:04
  2. Explainability1:03:38
  3. Governance and Security1:06:14

11. Running LLM Apps

  1. Guardrails and Safety1:16:16
  2. RAG in Production1:19:04
  3. Cost and Latency1:21:44

12. Data Pipelines and Features

  1. Data Pipelines1:24:33
  2. Point-in-Time Correctness1:27:12
  3. Feature Stores1:29:43

13. Optimisation and Scale

  1. Making Models Smaller1:32:10
  2. Batching and Hardware1:34:30
  3. Training at Scale1:36:57

14. ML Platforms

  1. ML Platforms1:39:33
  2. Pipelines on Kubernetes1:42:20
  3. Metrics That Matter1:45:03

15. Experiments, Labels, Forecasts

  1. Trustworthy Experiments1:46:59
  2. Labels and Feedback Loops1:49:53
  3. Forecasting Operations1:52:32

18. Deep Learning in Production

  1. Deep Learning Basics for Ops2:10:49
  2. Inference Servers2:13:25
  3. Edge and On-Device ML2:16:10

19. Cost and Practice

  1. FinOps for ML2:18:46
  2. Notebooks to Production2:21:34
  3. Team Practices2:24:00

20. Capstone: Build

  1. Capstone: The Plan2:26:28
  2. Capstone: The Pipeline2:28:01
  3. Capstone: The Gate2:29:40

21. Capstone: Ship and Operate

  1. Capstone: Serve2:31:34
  2. Capstone: Deliver2:33:12
  3. Capstone: Operate2:35:09

22. Security and Privacy Deep Dive

  1. The Danger in Model Files2:37:15
  2. Supply Chain Security2:39:43
  3. Privacy Techniques2:42:03

23. Streams, Labels, Packaging

  1. Streaming Features2:44:44
  2. Active Learning2:47:27
  3. Packaging Decisions2:50:01

24. Reference Architectures

  1. A Small Team Setup2:52:05
  2. A Growing Team Setup2:54:34
  3. Large Organisations2:57:01

25. Quiz Time and Interviews

  1. Quiz Time: The Foundations3:06:38
  2. Quiz Time: Production3:10:47
  3. Interview Preparation3:14:47

26. Cheat Sheet and Roadmap

  1. Myths, Glossary, Cheat Sheet3:17:08
  2. Your Roadmap3:20:07

27. Alerts, Dashboards, Slices

  1. Alerting on ML2:59:44
  2. Model Dashboards3:02:27
  3. Slice-Based Evaluation3:04:27

Useful links