100 Days of MLOps

The deep-dive MLOps track — 100 days from fundamentals and tooling to production pipelines, model serving on Kubernetes, monitoring, and scale.

44 of 100 days published.

The path

1. Understanding MLOps and Its Core Purpose 9 min read
2. Setting Up Your MLOps Development Environment 8 min read
3. Python Refresher for ML Engineering Tasks 13 min read
4. Version Control with Git for ML Projects 9 min read
5. Data Versioning & DVC Introduction 14 min read
6. DVC Pipelines & Experiment Tracking 12 min read
7. Review Week 1 & Build a Simple ML Project 11 min read
8. Experiment Tracking with MLflow 13 min read
9. MLflow Projects & Model Registry 15 min read
10. Hyperparameter Tuning & Experiment Comparison 12 min read
11. Docker for ML Engineers 16 min read
12. Writing Production-Grade Dockerfiles for ML 13 min read
13. Docker Compose for Multi-Service ML Environments 12 min read
14. Review Week 2 & The Containerized ML Experiment Workflow 10 min read
15. Model Serving Fundamentals & REST APIs 13 min read
16. Serving Models with MLflow Models Serve 12 min read
17. Introduction to Cloud Platforms for MLOps 9 min read
18. Cloud Storage for ML: S3 and GCS Basics 8 min read
19. Introduction to AWS SageMaker Training Jobs 19 min read
20. SageMaker Model Deployment & Endpoints 6 min read
21. Week 3 Review: The Cloud ML Workflow, End-to-End 11 min read
22. Introduction to Kubernetes for MLOps Engineers 11 min read
23. Deploying ML Models on Kubernetes 21 min read
24. Kubernetes ConfigMaps, Secrets, and Persistent Volumes for ML Models 19 min read
25. ML Model inference Scaling on Kubernetes Using Horizontal Pod Autoscaling and Rolling Updates 21 min read
26. Introduction to Helm for ML Application Packaging 30 min read
27. Introduction to Kubeflow for ML Pipelines 26 min read
28. Week 4 Review: Kubernetes and Kubeflow Foundations 30 min read
29. Building Kubeflow Pipelines with the Python SDK 20 min read
30. Running and Managing Kubeflow Pipeline Runs 10 min read
31. CI/CD Fundamentals for Machine Learning 28 min read
32. GitHub Actions for ML CI Pipelines 26 min read
33. Automated Model Training in CI/CD Pipelines 9 min read
34. Building and Pushing Docker Images in CI/CD 11 min read
35. Continuous Deployment: Automating ML Model Deployment 15 min read
36. Week 5 Review: CI/CD for ML Projects 7 min read
37. Feature Stores: Concepts and Use Cases 7 min read
38. Feast Online Store and Real-Time Feature Serving 6 min read
39. Data Validation with Great Expectations 6 min read
40. Data Drift Detection Fundamentals 5 min read
41. Introduction to Model Monitoring Concepts 5 min read
42. Week 6 Review: Data Quality and Monitoring Foundations 4 min read
43. Prometheus and Grafana for ML System Monitoring 4 min read
44. Custom ML Metrics with Prometheus 4 min read