Chapter 01: Introduction to MLOps🔗
"MLOps is the engineering discipline that takes ML from notebook to production — reliably, repeatedly, and at scale."
1.1 What is MLOps?🔗
MLOps (Machine Learning Operations) is the set of practices, tools, and culture that unifies Machine Learning development (ML Dev) with ML system deployment and operations (Ops). It is the discipline of automating and streamlining the entire machine learning lifecycle.
The Core Problem MLOps Solves🔗
WITHOUT MLOPS:
Data Scientist: "My model works! Accuracy 94%!"
Engineer: "It crashes in production."
Manager: "We trained 50 models last year. How many shipped?"
Data Scientist: "...3."
WITH MLOPS:
Data Scientist: "My model works! Accuracy 94%!"
CI/CD Pipeline: ✅ Tests pass → Docker build → K8s deploy → Monitoring live
Manager: "We shipped 48 of 50 models to production this year."
Official Definitions🔗
| Source | Definition |
|---|---|
| "MLOps is an ML engineering culture and practice that aims at unifying ML system development and ML system operation." | |
| Gartner | "A practice for collaboration and communication between data scientists and operations professionals to help manage production ML lifecycle." |
| Practitioners | "DevOps for Machine Learning — automate everything from data to deployment." |
1.2 The ML Production Gap🔗
85% of machine-learning models never make it to production, despite massive investment in building them. MLOps closes this gap.
┌────────────────────────────────────────────────────────────┐
│ WHY MODELS DON'T REACH PRODUCTION │
│ │
│ ❌ "Works on my machine" — environment mismatch │
│ ❌ No automated testing for data or model quality │
│ ❌ Manual, error-prone deployment processes │
│ ❌ No monitoring after deployment │
│ ❌ Siloed teams (DS vs Engineering vs Ops) │
│ ❌ No version control for data or models │
│ ❌ Reproducibility failures │
│ │
│ MLOps FIXES ALL OF THESE ✅ │
└────────────────────────────────────────────────────────────┘
1.3 DevOps vs DataOps vs MLOps vs LLMOps🔗
┌──────────────────────────────────────────────────────────────────────┐
│ OPERATIONS LANDSCAPE │
│ │
│ DevOps │
│ └── Automate software build → test → deploy │
│ Tools: Git, Jenkins, Docker, K8s │
│ │
│ DataOps │
│ └── Automate data pipeline quality & delivery │
│ Tools: Airflow, dbt, Great Expectations │
│ │
│ MLOps (= DevOps + DataOps + Model Management) │
│ └── Automate ML: data → train → evaluate → deploy → monitor │
│ Tools: DVC, MLflow, Kubeflow, Vertex AI │
│ │
│ LLMOps (MLOps for Large Language Models) │
│ └── Fine-tuning, RAG, Prompt management, LLM monitoring │
│ Tools: LangChain, vLLM, LlamaIndex, PromptLayer │
└──────────────────────────────────────────────────────────────────────┘
1.4 The Three Axes of MLOps🔗
PEOPLE
▲
│
│ ← MLOps lives
│ at the center
PROCESS ─────┼───── TECHNOLOGY
│
│
| Axis | What it covers |
|---|---|
| People | Data Scientists, ML Engineers, DataOps, DevOps, Platform Eng, Business |
| Process | Agile, CI/CD/CT/CM, code review, model approval workflows, SLAs |
| Technology | Git, Docker, K8s, MLflow, Airflow, Vertex AI, Prometheus |
1.5 MLOps Maturity Levels (Google Model)🔗
Google defines 3 levels — most enterprises sit at Level 0 or 1.
Level 0 — Manual, Script-Driven🔗
Data Scientist → Jupyter Notebook → .pkl file → manual deploy on server
- Everything done manually by one person
- No reproducibility — results can't be recreated
- No CI/CD — deployments are risky
- Model updated: when someone remembers
- Suitable for: Proof of concept / hackathon
Level 1 — ML Pipeline Automation🔗
New Data Trigger → Data Pipeline → Training Pipeline → Auto-evaluate → Model Registry
│
Auto deploy to serving
- Training pipeline is automated end-to-end
- Models automatically evaluated against baseline
- Feature engineering is consistent (feature store)
- Models versioned in registry
- Suitable for: Small teams with stable models
Level 2 — Full CI/CD + CT Pipeline Automation🔗
Code Change → CI: test pipeline code → CD: deploy new pipeline → CT: retrain on new data
- CI/CD for the pipeline code itself (not just models)
- Multiple ML teams can iterate rapidly
- Automated drift detection triggers retraining
- Full observability and alerting
- Suitable for: Enterprise, large ML teams, regulated industries
Level 3 — Self-Optimizing Systems (Emerging)🔗
Production feedback → Auto HPO → Auto architecture search → Auto deploy → Monitor → repeat
- Models that continuously improve themselves
- Automated architecture search in production
- Fully autonomous retraining and promotion
- Suitable for: Advanced research/production teams
1.6 Key MLOps Concepts Explained Simply🔗
| Concept | Simple Definition | Example |
|---|---|---|
| CI | Auto-test code on every commit | pytest runs on every git push |
| CD | Auto-deploy trained model | Jenkins deploys to K8s on merge |
| CT | Auto-retrain on new data | Airflow DAG triggers weekly retraining |
| CM | Monitor model in production | Grafana alerts if accuracy drops |
| Feature Store | Central repo for ML features | Feast serving features to training + inference |
| Model Registry | Versioned storage for models | MLflow Registry with Staging/Production stages |
| Data Drift | Input features shift from training distribution | Users' age distribution changes over time |
| Concept Drift | Label relationship changes | "Fraud" patterns evolve, old model no longer accurate |
1.7 MLOps Business Value🔗
┌──────────────────────────────────────────────────────┐
│ MLOPS ROI │
│ │
│ Without MLOps: │
│ Time to deploy new model: weeks to months │
│ Model failures in prod: frequent │
│ Data scientist time on ops: ~60% │
│ Models in production: ~15% │
│ │
│ With MLOps: │
│ Time to deploy new model: hours to days │
│ Model failures in prod: rare, auto-detected │
│ Data scientist time on ops: ~10% │
│ Models in production: ~70%+ │
│ │
│ Market: $1.58B in 2024 → $2.33B in 2025 (CAGR 35%) │
└──────────────────────────────────────────────────────┘