🧠 What is MLOps?¶
📌 Definition¶
What is MLOps?¶
MLOps (Machine Learning Operations) is the practice of applying DevOps principles to the machine learning lifecycle — combining collaboration, automation, and governance to reliably take ML models from experimentation to production.
It focuses on making ML workflows repeatable, auditable, and scalable, especially in production environments.
🧭 Scope of MLOps¶
What does MLOps cover?¶
MLOps spans the entire machine learning lifecycle, not just deployment:
- Data: ingestion, validation, versioning
- Modeling: training, tuning, experiment tracking
- Packaging: reproducible scripts, containerization
- Deployment: serving models as APIs or batch jobs
- Monitoring: detecting data drift, model decay, logging behavior
- Feedback Loops: closing the loop from real-world usage to retraining
MLOps is not limited to infra teams — data scientists, ML engineers, and platform teams all play a role.
🔁 MLOps vs DevOps vs DataOps¶
🪞 Key Differences¶
Comparing MLOps, DevOps, and DataOps¶
| Aspect | DevOps | DataOps | MLOps |
|---|---|---|---|
| Focus | Software delivery & CI/CD | Data pipelines & data quality | ML model lifecycle & reliability |
| Primary Unit | Code (apps, services) | Data (ETL, validation, lineage) | Models (training, deployment, monitoring) |
| Key Roles | Developers, DevOps engineers | Data engineers, analysts | Data scientists, ML engineers |
| Tooling | GitHub Actions, Jenkins, Docker | Airflow, dbt, Great Expectations | MLflow, TFX, SageMaker, KubeFlow |
Each discipline solves a different bottleneck, but they all aim for automation, reproducibility, and scale in production systems.
🔄 Overlaps & Confusions¶
Why These Terms Often Get Mixed Up¶
- DevOps and MLOps both use CI/CD, version control, and containerization — but MLOps adds data + model tracking.
- MLOps and DataOps both rely on good data practices — but DataOps stops short of modeling or deployment.
- In practice, MLOps sits at the intersection of both: you need DevOps tooling and DataOps discipline to do MLOps right.
❓ Why MLOps Matters¶
🧨 Real Pain Points¶
Problems That Arise Without MLOps¶
- No reproducibility: Results from notebooks can't be replicated in production.
- Ad hoc handoffs: Models are thrown over the wall to engineers with minimal documentation.
- Silent model decay: Performance drops over time due to changing data, but no one notices.
- Versioning chaos: Confusion over which model or dataset was used in production.
- Environment mismatches: Code works locally but fails in staging or production due to dependency drift.
💥 Failures Without MLOps¶
Realistic Consequences¶
- A model trained on clean, idealized data fails miserably when exposed to real user behavior.
- A one-off script introduces a bug during retraining, and no one notices for weeks.
- A business team makes decisions based on stale model predictions without knowing the model is outdated.
- Multiple teams unknowingly deploy slightly different versions of the “same” model.
These failures aren’t rare — they’re default behavior without MLOps practices in place.
🌀 MLOps Lifecycle¶
🧱 High-Level Stages¶
The Core Lifecycle of MLOps¶
Data Collection & Validation
- Source data is ingested, cleaned, profiled, and versioned.
Model Development
- Includes training, tuning, and evaluating models — often in notebooks or scripts.
Packaging & Testing
- Code and models are serialized, containerized, and tested for portability.
Deployment
- Models are exposed via APIs, batch jobs, or integrated into apps.
Monitoring
- Track predictions, usage metrics, model drift, and data quality.
Governance & Compliance
- Logging, reproducibility, access control, and auditability across stages.
🔄 Feedback Loops¶
From Real World → Retraining¶
- Monitoring feeds retraining: Real-time logs, drift metrics, and model errors inform the next training cycle.
- Closed-loop learning: Label feedback, human-in-the-loop corrections, or delayed outcomes are folded into future model updates.
- Versioned iterations: Each training cycle builds on a known version of code, data, and parameters.
MLOps ensures this loop is automated and reliable, not manual or reactive.
🏗️ Stages of MLOps¶
👥 Who Uses MLOps?¶
🧑🔬 Personas¶
Key Roles Involved in MLOps¶
- Data Scientist: Develops models, interprets results, defines metrics.
- ML Engineer: Productionizes models, builds pipelines, handles scalability.
- Data Engineer: Manages data pipelines, transformations, and storage.
- DevOps/Infra Engineer: Maintains infrastructure, automates CI/CD, ensures reliability.
- Product Owner / Analyst: Provides context, evaluates outcomes, communicates needs.
MLOps is cross-functional by design — no single person owns it end-to-end.
🧭 Responsibility Split¶
Who Owns What?¶
| Stage | Primary Owner(s) |
|---|---|
| Data Ingestion | Data Engineer |
| Feature Engineering | Data Scientist, Data Engineer |
| Model Training | Data Scientist |
| Experiment Tracking | Data Scientist, ML Engineer |
| Deployment | ML Engineer, DevOps |
| Monitoring | ML Engineer, Infra/DevOps |
| Feedback Loop | Data Scientist, Product, ML Engineer |
Responsibility often overlaps — coordination is key to avoid blind spots.
🧰 Common Tools by Stage¶
🧪 Training + Tracking¶
Tools That Support Model Development¶
- MLflow – Tracks experiments, parameters, artifacts, and metrics.
- Weights & Biases (W&B) – Visual experiment dashboard, collaboration features.
- DVC – Version control for datasets and model artifacts.
- Optuna / Ray Tune – Hyperparameter tuning frameworks.
📊 Monitoring¶
Tools for Post-Deployment Visibility¶
- Prometheus + Grafana – Collect and visualize performance metrics.
- WhyLabs / Evidently – Detect drift, monitor data and model health.
- ELK Stack (Elasticsearch, Logstash, Kibana) – Log aggregation and search.
- Custom Dashboards – Streamlit, Superset, or internal tools for surfacing signals.
📶 MLOps Maturity Levels¶
🔢 Level 1 – Partial Automation¶
Some Repeatability and Tracking¶
- Training moved to scripts with basic automation.
- Experiments tracked using MLflow or W&B.
- Models deployed with basic CI/CD or manual API wrapping.
- Some monitoring exists, but limited in scope.
Teams at this stage are usually growing or scaling up.
🔢 Level 2 – Full Automation¶
End-to-End MLOps Pipeline¶
- Data, code, and model artifacts are fully versioned.
- CI/CD automates training, testing, and deployment.
- Monitoring alerts trigger retraining or rollback.
- Model registry, approval flows, and rollback strategies in place.
This level is typical for mature ML product teams or platformized orgs.
🧑💻 How Much Should a Data Scientist Know?¶
⚖️ Breadth vs Depth¶
You Don’t Need to Be an MLE¶
A data scientist doesn’t need to master infra or deployment tools, but should:
- Understand the full ML lifecycle — not just modeling.
- Collaborate effectively with engineers by knowing the basics of CI/CD, Docker, and model serving.
- Speak the language of operations and recognize production constraints.
🧱 Minimum Baseline¶
What You Should Know to Be Dangerous¶
- How your model will be used: batch vs real-time, latency limits, API vs offline job.
- What can break: data drift, retraining issues, environment mismatches.
- How to track your work: MLflow, version control, reproducible scripts.
- How to communicate handoffs: clear documentation, artifacts, config files.
You don’t need to build the MLOps stack — but you should build for it.
✅ Summary¶
🔁 Key Takeaways¶
- MLOps is not just deployment — it covers the full ML lifecycle from data to feedback loops.
- It brings structure, repeatability, and monitoring to what is often an ad hoc process.
- MLOps is collaborative — no single person owns it end-to-end.
- You don’t need to be an infra expert, but you should design with production in mind.
📍 What's Next¶
In the next notebook (02_Model_Packaging.ipynb), we’ll move from theory to practice:
- Structure your ML projects for reuse.
- Save models in a portable format.
- Create training scripts and basic CLI interfaces.
- Learn the basics of containerization with Docker.
This begins the transition from experimentation → deployable asset.