The MLOps Mastery Series

Not a roadmap. Not a list of links. This is a senior-engineer-level curriculum — 12 deep-dive modules on the full MLOps / AIOps stack, written like engineering documentation: architecture, annotated code, hands-on projects, and real interview preparation.

Each module is a pair of hand-crafted, self-contained HTML documents. No build step, no dependencies, works fully offline, with a built-in dark/light theme toggle.

What's Inside

12 modules, each containing:

• <topic>-mastery.html — a structured deep dive: chapters, annotated code, architecture deep-dives, hands-on projects, and senior-level interview Q&A.
• glossary.html — a searchable, A-to-Z reference of every key term, concept, and pattern for the topic.

This is production content, not tutorials — it assumes you already ship software and want to operate at the senior AIOps / ML-engineer level.

Modules

#	Module	Mastery Guide	Glossary	What You'll Learn
1	Dagster	dagster-mastery.html	glossary.html	Production data platform orchestration: architecture, assets, jobs, sensors, and senior-level patterns
2	Apache Airflow	airflow-mastery.html	glossary.html	From first DAG to production-grade MLOps workflows: operators, scheduling, executors, and CI/CD
3	Apache Kafka	kafka-mastery.html	glossary.html	Event streaming for AIOps: producers, consumers, Kafka Streams, Connect, and Schema Registry
4	Docker & Kubernetes	docker-k8s-mastery.html	glossary.html	Containers to production clusters — the infrastructure backbone every ML engineer must own
5	DVC	dvc-mastery.html	glossary.html	Data & model versioning with Git-like workflows for AIOps pipelines
6	FastAPI	fastapi-mastery.html	glossary.html	From first endpoint to production ML APIs: async, validation, deployment, and scaling
7	Kubeflow	kubeflow-mastery.html	glossary.html	The Kubernetes-native ML platform: Pipelines, KServe, Katib, Notebooks, and MLOps on K8s
8	LangChain & LangGraph	langchain-mastery.html	glossary.html	Production LLM applications: chains, RAG, agents, LangGraph, memory, and deployment patterns
9	MLflow	mlflow-mastery.html	glossary.html	From experiment tracking to production model serving — the complete ML lifecycle deep dive
10	Prometheus & Grafana	prometheus-grafana-mastery.html	glossary.html	Complete observability stack: metrics, PromQL, alerting, dashboards, and ML infra monitoring
11	Terraform	terraform-mastery.html	glossary.html	Infrastructure as Code: from first resource to production-grade cloud infrastructure
12	Weights & Biases	wandb-mastery.html	glossary.html	Experiment tracking, hyperparameter sweeps, model registry, and production monitoring

Suggested Reading Order

The modules are self-contained, so you can jump in anywhere. If you're building up from the ground, a recommended order:

Stage 1 — Foundations Docker & Kubernetes → Terraform → FastAPI

Stage 2 — Data, Versioning & Orchestration Apache Kafka → DVC → Airflow → Dagster

Stage 3 — ML Experimentation & Platforms MLflow → Weights & Biases → Kubeflow

Stage 4 — LLM Applications LangChain & LangGraph

Stage 5 — Production & Observability Prometheus & Grafana

How to Use

1. Clone or download the repo.
2. Open any *-mastery.html file in a browser and work through the chapters.
3. When you hit a term you're unsure of, open that module's glossary.html and search it.
4. Follow along with the code in each chapter — the mastery guides include real projects and interview Q&A to test yourself.

Repository Structure

mlops-mastery-series/
├── README.md
├── Dagster/
│   ├── dagster-mastery.html
│   └── glossary.html
├── Airflow/
│   ├── airflow-mastery.html
│   └── glossary.html
├── ...
└── Weights-and-Biases/
    ├── wandb-mastery.html
    └── glossary.html