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🚀 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🔗

🛠️ 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