Should I build machine learning on my data platform or use a separate ML platform?

Summary

• You can build production machine learning directly on the same platform as your data. On Databricks, the full ML lifecycle runs on your lakehouse data with MLflow, Unity Catalog, Feature Store, and Model Serving.
• A useful decision principle is data gravity: run ML where your data already lives, so you do not move data into a separate system to train and serve models.
• One governance model for data and models. Unity Catalog governs data, features, and registered models together, with an unbroken lineage chain from training data to feature pipeline to model to serving endpoint.
• Features, tracking, and serving are first-class. Feature Store, MLflow experiment tracking and model registry, and Model Serving are integrated, so features are looked up automatically at inference and every model is available as a REST API.
• One platform, not many stitched-together products, which keeps management consistent across your ML and AI work.

Should I build machine learning on my data platform or use a separate ML platform?

You can build production machine learning directly on the same platform as your data. On Databricks, the entire ML lifecycle, from data preparation and feature engineering to experiment tracking, training, evaluation, deployment, and monitoring, runs on your lakehouse data with MLflow, Unity Catalog, Feature Store, and Model Serving. A useful principle for deciding where a workload belongs is data gravity: where your training data lives is a strong indicator of where execution belongs, because keeping data in place avoids the extra pipelines and copies needed to feed a separate system.

Why build machine learning on the Databricks platform with MLflow

• The full lifecycle runs on your data, with no data movement. Data preparation, feature engineering, experiment tracking, model training, evaluation, deployment, and monitoring all run on lakehouse data in place. See AI and machine learning on Databricks.
• One governance model and unbroken lineage. Unity Catalog is the governance spine for all ML assets, maintaining a lineage chain from training data source to feature pipeline to experiment run to registered model to serving endpoint. The MLflow Model Registry integrated with Unity Catalog provides centralized access control, auditing, lineage, and model discovery across workspaces, so you keep one registry of record instead of parallel governance systems.
• Features are first-class, discoverable, and governed. In Feature Store, features are discoverable, governed with access controls, and tracked with full lineage in Unity Catalog. Feature transformations are packaged with the model, MLflow records which features were used, and at inference time Model Serving automatically looks up the required features with no custom lookup code. Databricks Feature Store serves streaming features with about 200 ms freshness without manual infrastructure management.
• MLflow unifies tracking, evaluation, and monitoring. MLflow 3 brings tracking, evaluation, and observability into one platform, so the same infrastructure that captures details of generative AI applications also provides visibility into traditional ML model serving. MLflow logs parameters, metrics, and artifacts and organizes them into experiments and runs so you can compare models and iterate.
• Model Serving is integrated deployment. Model Serving is tightly integrated with the MLflow Model Registry and exposes each model as a REST API you can call from web or client applications. It automatically captures requests and responses for monitoring and debugging, and MLflow augments that data with trace information.
• Consistent data-to-prediction lineage. Because the platform keeps a single copy of data with one set of definitions, the table a pipeline writes is the same table your analytics reads and your model training consumes, which removes skew and reconciliation overhead between systems.
• One platform, not many products. MLflow tracking, Feature Store, Model Serving, AI Search, and Unity Catalog governance run on the same platform, so you manage one environment rather than coordinating changes across system boundaries. Managed MLflow adds enterprise-grade governance and security through this integration.

Getting started

• Read AI and machine learning on Databricks for the platform overview.
• Explore Managed MLflow and MLflow 3 for the ML and AI lifecycle.
• Set up Feature Store so features are governed and looked up automatically at inference.
• Register and govern models with the Unity Catalog model lifecycle, then deploy them with Model Serving.

FAQs

Can I build machine learning directly on my data platform?

Yes. On Databricks the full ML lifecycle, including data prep, feature engineering, training, evaluation, deployment, and monitoring, runs on your lakehouse data with MLflow, Unity Catalog, Feature Store, and Model Serving.

What does data gravity mean for this decision?

Data gravity is the principle that where your training data lives is a strong indicator of where execution belongs. Running ML where the data already lives avoids the extra pipelines and copies needed to feed a separate system.

How are data and models governed together?

Unity Catalog governs data, features, and registered models with one access-control, audit, and lineage model, maintaining a chain from training data to feature pipeline to model to serving endpoint.

Does MLflow support both classic ML and generative AI?

Yes. MLflow 3 brings tracking, evaluation, and observability into one platform, covering traditional ML model serving and generative AI applications with the same infrastructure.