What is MLOps and what capabilities does a modern MLOps platform need?

Summary

• MLOps is a set of processes and automated steps for managing the code, data, and models behind machine learning systems to improve their performance, stability, and long-term efficiency.
• It combines DevOps, DataOps, and ModelOps so all three of code, data, and models are governed, versioned, and monitored together, and it adds Continuous Training to automatically retrain models when data or requirements change.
• A modern MLOps platform needs experiment tracking, a model registry with versioning, a feature store, workflow orchestration, model serving and deployment, monitoring and observability, data governance, and CI/CD for ML.
• Databricks delivers all three Ops disciplines on one platform: Unity Catalog for data and model governance, MLflow for experiment tracking, model registry, and serving, and Declarative Automation Bundles with Git for CI/CD.
• Managed MLflow runs the full ML lifecycle on your lakehouse data without moving it to a separate system.

What is MLOps and what capabilities does a modern MLOps platform need?

MLOps is a set of processes and automated steps for managing the code, data, and models behind machine learning systems, with the goal of improving their performance, stability, and long-term efficiency. It brings together three disciplines: DevOps (for code), DataOps (for data), and ModelOps (for models). All three must be governed, versioned, and monitored at the same time. MLOps extends the software lifecycle with Continuous Training (CT), which automates model retraining when data distributions shift or business requirements change, because models degrade in accuracy as new data arrives. ML assets progress through stages from early development with loose controls, through testing, to tightly controlled production. See What is MLOps?.

What capabilities a modern MLOps platform needs

• Experiment tracking. The foundation of MLOps. Teams run many training iterations across algorithms, hyperparameters, and features; systematic tracking of metrics, parameters, and code versions per run creates a reproducible, searchable audit trail so teams can compare models and promote the best version.
• Model registry and versioning. A central store where trained models are catalogued, versioned, and transitioned through lifecycle stages such as staging, validation, production, and archival, so teams can roll back a degrading model to a prior version quickly.
• Feature store. Centralizes computation and storage of features so the same transformations used for training are applied at inference time, addressing training-serving skew.
• Workflow orchestration. Automates multi-step pipelines from data ingestion and preprocessing through training, validation, and deployment, managing dependencies, handling failures, and providing pipeline visibility.
• Model serving and deployment. Packages models and exposes them as APIs, covering real-time low-latency inference and batch inference, with support for scaling, A/B testing, and canary deployments.
• Model monitoring and observability. Continuously tracks model performance, data drift, prediction distributions, and downstream business metrics after deployment.
• Data preparation and governance. Manages feature tables, training sets, and inference outputs with unified access control and lineage tracking.
• CI/CD for ML. Automates testing and deployment of ML code and infrastructure through version-controlled repositories.

Why Databricks and MLflow for MLOps

Databricks provides an integrated platform that combines all three Ops disciplines on the same platform that handles SQL analytics and data engineering. The full ML lifecycle, from data preparation and feature engineering through experiment tracking, training, evaluation, deployment, and monitoring, runs on lakehouse data without moving it to a separate system. See ML model development with MLflow.

• DataOps with Unity Catalog. Feature tables, training sets, and inference outputs are governed with unified access control, lineage tracking, and model discovery for reuse across workspaces.
• ModelOps with MLflow. MLflow provides experiment tracking (logging parameters, metrics, and artifacts), a model registry with staging and production lifecycle stages and versioning, and a standard model packaging format that abstracts over popular ML frameworks so models can be served to REST API endpoints and batch jobs. MLflow is a widely adopted open-source MLOps framework, originally created at Databricks and donated to the Linux Foundation.
• Model Serving. Deploys models as REST API endpoints with automatic request and response capture for monitoring and debugging.
• DevOps with MLOps Stacks and Git. MLOps Stacks provides infrastructure-as-code templates for repeatable promotion from development to production, with the model development process saved and tracked as code in a source-controlled repository.
• Orchestration and gateway governance. Lakeflow Jobs orchestrates multi-step ML workflows as scheduled or triggered pipelines, and the AI Gateway provides centralized governance and monitoring for model endpoints, including rate limits, usage tracking, and payload logging. See MLOps and governance.

Getting started

• Read What is MLOps? for the workflow and asset lifecycle.
• Review MLOps and governance to map capabilities to Databricks tools.
• Use MLOps Stacks to bootstrap a repeatable dev-to-prod setup as code.
• Explore The Big Book of MLOps and the complete guide to MLOps frameworks.

FAQs

How is MLOps different from DevOps?

MLOps builds on DevOps by adding the governance and lifecycle management of data and models, not just code. It introduces Continuous Training to retrain models automatically when data or requirements change, since models degrade in accuracy over time while code does not.

What are the core components of an MLOps platform?

Experiment tracking, a model registry with versioning, a feature store, workflow orchestration, model serving and deployment, monitoring and observability, data governance, and CI/CD for ML.

What is MLflow?

MLflow is a widely adopted open-source MLOps framework created at Databricks and donated to the Linux Foundation. It provides experiment tracking, a model registry, a standard model packaging format, and reproducible project packaging.

How does Databricks deliver MLOps?

Databricks combines DataOps (Unity Catalog), ModelOps (MLflow and Model Serving), and DevOps (MLOps Stacks and Git) on one platform, running the full ML lifecycle on lakehouse data.