Lilly Tech Systems
Introduction to MLOps
Understand what MLOps is, why organizations need it, the maturity model, key roles, and how it compares to DevOps and DataOps.
What is MLOps?
MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering to deploy and maintain ML systems in production reliably and efficiently. It bridges the gap between ML model development and production deployment.
While data scientists can build highly accurate models in notebooks, getting those models into production — and keeping them running — requires a fundamentally different set of skills and infrastructure.
Why MLOps Matters
Without MLOps, organizations face several challenges:
- Deployment bottleneck: Models sit in notebooks and never reach production.
- Reproducibility issues: Teams cannot recreate results from weeks ago because environments, data, and code have changed.
- Silent model degradation: Models decay in production as data distributions shift, but nobody notices until business metrics tank.
- Manual processes: Retraining, testing, and deploying models requires manual intervention at every step.
- Collaboration friction: Data scientists, ML engineers, and platform engineers work in silos.
MLOps Maturity Levels
Google introduced a widely adopted MLOps maturity model with five levels:
| Level | Name | Description |
|---|---|---|
| 0 | Manual | Everything is manual: data prep, training, validation, deployment. Script-driven, notebook-based. |
| 1 | ML Pipeline Automation | Automated ML pipeline for continuous training. Data and model validation steps in place. |
| 2 | CI/CD Pipeline Automation | Automated CI/CD for building, testing, and deploying ML pipelines themselves. Rapid, reliable iterations. |
| 3 | Automated Retraining | Trigger-based retraining (schedule, data drift, performance drop). Full monitoring and alerting. |
| 4 | Full MLOps | Fully automated, self-healing ML systems. Feature stores, A/B testing, shadow deployments, governance. |
Key Roles in MLOps
ML Engineer
Bridges data science and engineering. Builds training pipelines, optimizes models for production, and manages model serving infrastructure.
Data Engineer
Builds and maintains data pipelines, manages data quality, creates feature stores, and ensures data is available and reliable for ML.
Platform Engineer
Builds and manages the ML platform infrastructure: compute clusters, model serving, monitoring, and CI/CD pipelines.
Data Scientist
Develops ML models, runs experiments, performs feature engineering, and evaluates model performance. Collaborates with ML engineers for production.
MLOps vs DevOps vs DataOps
| Aspect | DevOps | DataOps | MLOps |
|---|---|---|---|
| Focus | Software delivery | Data pipeline quality | ML model lifecycle |
| Artifacts | Code, binaries | Data, schemas | Models, data, code |
| Testing | Unit, integration, E2E | Data quality, schema | Model performance, data drift |
| Versioning | Code (Git) | Data, pipelines | Code, data, models, experiments |
| Monitoring | App performance, errors | Data freshness, quality | Model accuracy, drift, latency |
| Rollback | Previous code version | Previous data version | Previous model version + data |
The MLOps Tools Landscape
The MLOps ecosystem is rich and growing. Here are the key categories and popular tools:
| Category | Tools |
|---|---|
| Experiment Tracking | MLflow, Weights & Biases, Neptune, CometML |
| Data Versioning | DVC, LakeFS, Delta Lake, Pachyderm |
| Feature Store | Feast, Tecton, Hopsworks, Amazon SageMaker Feature Store |
| Orchestration | Apache Airflow, Kubeflow Pipelines, Prefect, Dagster |
| Model Serving | TFServing, TorchServe, Triton, BentoML, Seldon |
| Monitoring | Evidently AI, WhyLabs, Arize, Fiddler |
| End-to-End Platforms | AWS SageMaker, Google Vertex AI, Azure ML, Databricks |