🚀 Complete MLOps Tutorial — The Definitive Guide🔗

One place. Every concept. Production-ready. Updated 2024–2025.

Covering: Git • DVC • Jenkins • Docker • Kubernetes • GCP Vertex AI • Airflow • Kubeflow • MLflow • Weights & Biases • Feature Stores • Model Serving • Monitoring • LLMOps • Governance • Security • and more.

🗺️ Master Architecture — The MLOps Universe🔗

┌──────────────────────────────────────────────────────────────────────────────┐
│                         THE MLOPS UNIVERSE                                   │
│                                                                              │
│  ① SOURCE        ② DATA           ③ EXPERIMENT     ④ CI/CD                  │
│  CONTROL         LAYER            LAYER            LAYER                    │
│  ┌──────────┐   ┌─────────────┐  ┌────────────┐   ┌──────────────────┐     │
│  │Git/GitHub│   │Feature Store│  │MLflow / W&B│   │Jenkins/GH Actions│     │
│  │DVC       │──▶│GCS/BigQuery │─▶│Optuna/NAS  │──▶│Cloud Build/ArgoCD│     │
│  └──────────┘   │Great Expect.│  │ClearML     │   └────────┬─────────┘     │
│                 └─────────────┘  └────────────┘            │               │
│                                                             ▼               │
│  ⑧ GOVERN        ⑦ MONITOR        ⑥ SERVE          ⑤ DEPLOY                │
│  ┌──────────┐   ┌─────────────┐  ┌────────────┐   ┌──────────────────┐     │
│  │Responsible│  │Prometheus   │  │TF Serving  │   │Docker/K8s/GKE    │     │
│  │AI/SHAP   │◀──│Grafana      │◀─│Triton      │◀──│Vertex AI Endpoint│     │
│  │EU AI Act │   │Evidently    │  │KServe      │   │Cloud Run         │     │
│  └──────────┘   │Fiddler      │  │Seldon Core │   └──────────────────┘     │
│                 └─────────────┘  └────────────┘                            │
│                                                                              │
│  ──────────── ORCHESTRATION: Airflow • Kubeflow • Prefect • Vertex Pipelines│
│  ──────────── DISTRIBUTED: Ray • Horovod • Spark                            │
│  ──────────── LLMOps: RAG • Fine-tuning • Prompt Ops • LangChain            │
└──────────────────────────────────────────────────────────────────────────────┘

📚 Complete Table of Contents🔗

🔵 FOUNDATIONS🔗

#	Chapter	Key Topics
01	Introduction to MLOps	What, Why, DevOps vs MLOps, Maturity Levels 0–3, Roles
02	ML Lifecycle & Pipeline	8 Stages, Artifacts, Feature Store intro, Training-Serving Skew
03	MLOps Roles & Team Structure	DS, MLE, MLOps Eng, DataOps, Personas, Collaboration model

🟢 VERSION CONTROL & DATA MANAGEMENT🔗

#	Chapter	Key Topics
04	Git & GitHub	GitFlow, PRs, GitHub Actions, Branching, Secrets
05	DVC — Data Version Control	DVC pipelines, Remote storage, Reproducibility, Params
06	Data Quality & Validation	Great Expectations, TFX Data Validation, Pandera, Schema

🟡 EXPERIMENT TRACKING & MODEL MANAGEMENT🔗

#	Chapter	Key Topics
07	MLflow	Tracking, Projects, Models, Registry, UI, MLflow + Jenkins
08	Weights & Biases (W&B)	Runs, Sweeps, Artifacts, Reports, W&B vs MLflow
09	ClearML & Neptune.ai	ClearML tasks, auto-logging, Neptune experiments
10	AutoML & HPO	AutoML concepts, Optuna, Ray Tune, NAS, Vertex AI AutoML

🟠 CI/CD & PIPELINE ORCHESTRATION🔗

#	Chapter	Key Topics
11	CI/CD for ML	Testing pyramid, Quality gates, Promotion flow, Triggers
12	Jenkins	Architecture, Jenkinsfile, Plugins, Agents, Blue Ocean
13	Apache Airflow	DAGs, Operators, Sensors, GCP operators, Scheduling
14	Kubeflow Pipelines	KFP SDK, Components, Pipelines, Metadata, KServe
15	Vertex AI Pipelines	Vertex Pipelines, TFX, Kubeflow on GCP, scheduling
16	Prefect & ZenML	Prefect flows, ZenML stacks, comparison

🔴 CONTAINERIZATION & INFRASTRUCTURE🔗

#	Chapter	Key Topics
17	Docker for MLOps	Dockerfile, Multi-stage, Compose, Registry, Best practices
18	Kubernetes (K8s)	Architecture, Deployments, Services, HPA, RBAC, GKE
19	Distributed Training	Ray, Horovod, PyTorch DDP, Vertex AI Training, GPU

🟣 FEATURE STORES🔗

#	Chapter	Key Topics
20	Feature Stores	Why Feature Stores, Feast, Tecton, Vertex AI Feature Store, Hopsworks

☁️ CLOUD PLATFORMS🔗

#	Chapter	Key Topics
21	GCP & Vertex AI — Deep Dive	GCS, GKE, GAR, Cloud Build, Vertex AI complete suite
22	AWS SageMaker	SageMaker Studio, Pipelines, Endpoints, Feature Store
23	Azure ML	Azure ML Studio, Pipelines, Endpoints, Responsible AI
24	Multi-Cloud MLOps	Patterns, Trade-offs, Portability strategies

🌐 MODEL SERVING🔗

#	Chapter	Key Topics
25	Model Serving Strategies	Online, Batch, Streaming, A/B, Canary, Shadow, Blue/Green
26	Serving Frameworks	TF Serving, TorchServe, Triton, Seldon, KServe, FastAPI
27	Vertex AI Endpoints	Online prediction, Batch prediction, Explainability, Traffic split

📊 MONITORING & OBSERVABILITY🔗

#	Chapter	Key Topics
28	Model Monitoring	Drift types, Metrics, Prometheus, Grafana, Alerting
29	Data & Model Drift Detection	Evidently AI, Alibi-Detect, statistical tests, PSI, KL divergence
30	Metadata & Lineage	ML Metadata, MLMD, Vertex ML Metadata, lineage tracking

🛡️ GOVERNANCE, SECURITY & RESPONSIBLE AI🔗

#	Chapter	Key Topics
31	Responsible AI & Explainability	SHAP, LIME, Fairness, Bias detection, What-If Tool
32	MLOps Security	RBAC, Secrets, IAM, Model security, Supply chain
33	Model Governance & Compliance	Model cards, Audit trails, EU AI Act, NIST RMF

🤖 LLMOPS & ADVANCED TOPICS🔗

#	Chapter	Key Topics
34	LLMOps	LLM lifecycle, Fine-tuning, RAG, Prompt Ops, Evaluation
35	Edge ML	TFLite, ONNX, Model optimization, IoT deployment
36	Cost Optimization in MLOps	Compute costs, Spot instances, Caching, Right-sizing

🏁 CAPSTONE🔗

#	Chapter	Key Topics
37	End-to-End Project	Full pipeline: Git→Jenkins→Docker→GKE→Vertex AI→Monitor
38	MLOps Tools Comparison	Side-by-side comparison of all tools by category

📖 REFERENCE🔗

Cheatsheet — All CLI commands in one place
Glossary — 100+ MLOps terms defined

🛠️ The Modern MLOps Stack at a Glance🔗

LAYER              │ OPEN SOURCE              │ GCP MANAGED
───────────────────┼──────────────────────────┼───────────────────────────
Source Control     │ Git, GitHub              │ Cloud Source Repositories
Data Storage       │ MinIO, HDFS              │ GCS, BigQuery
Data Versioning    │ DVC, LakeFS              │ Vertex AI Datasets
Data Quality       │ Great Expectations       │ Dataplex, TFX TFDV
Feature Store      │ Feast, Hopsworks         │ Vertex AI Feature Store
Experiment Track   │ MLflow, W&B (free)       │ Vertex AI Experiments
Orchestration      │ Airflow, Prefect, ZenML  │ Cloud Composer, Vertex AI Pipelines
Training           │ PyTorch, TF, scikit-learn│ Vertex AI Training, AutoML
HPO                │ Optuna, Ray Tune         │ Vertex AI Vizier
Container Build    │ Docker, Buildah          │ Cloud Build, Artifact Registry
Container Reg.     │ Docker Hub, Harbor       │ Artifact Registry (GAR)
Deployment         │ Kubernetes, Seldon       │ GKE, Vertex AI Endpoints
Serving            │ TF Serving, Triton       │ Vertex AI Prediction
Monitoring         │ Prometheus, Grafana      │ Cloud Monitoring, Vertex AI Monitoring
Drift Detection    │ Evidently, Alibi         │ Vertex AI Model Monitoring
Metadata/Lineage   │ MLMD, MLflow             │ Vertex ML Metadata
CI/CD              │ Jenkins, GitHub Actions  │ Cloud Build, Cloud Deploy
LLMOps             │ LangChain, vLLM          │ Vertex AI (Gemini), Model Garden

Begin with Chapter 01 → Introduction to MLOps