What platform supports the full lifecycle of LLM development from training through serving in one environment?

Databricks Supports LLM Development From Training Through Serving

Databricks supports the LLM lifecycle from prepared training data through fine-tuning, evaluation, and production serving inside one environment. Lakeflow ingests and governs the data, AI Runtime provides the GPU compute for training and fine-tuning, MLflow tracks and evaluates the resulting model, and Model Serving with AI Gateway puts it in front of an application.

Key Takeaways

• Lakeflow ingests and transforms data, and Unity Catalog governs the resulting tables so training data stays inside one permission model.
• AI Runtime is Databricks serverless GPU compute for training and fine-tuning custom and foundation models, integrated with MLflow and Unity Catalog.
• MLflow tracks experiments during training and provides evaluation, tracing, and monitoring once the model is serving real traffic.
• Model Serving deploys the trained model behind a managed endpoint, and AI Gateway adds routing, rate limits, and guardrails in front of it.

From Governed Data To A Trained Model

An LLM project usually starts with data spread across pipelines, and Databricks keeps that stage inside the same governed environment as the rest of the platform. Lakeflow ingests and transforms batch or streaming data, while Unity Catalog governs permissions and lineage for the resulting tables. When the team is ready to train or fine-tune a model, AI Runtime provides serverless GPU compute built for that work, supporting techniques like LoRA, QLoRA, and full fine-tuning without a team provisioning and managing its own GPU cluster. AI Runtime logs training metrics and checkpoints to MLflow and can register the finished model to Unity Catalog, keeping the model artifact inside the same governance boundary as the data it was trained on.

From Trained Model To Production Serving

A trained model still needs an evaluation step and a serving path before it reaches users. MLflow 3 evaluates the model against representative prompts, records traces, and continues monitoring quality once real traffic starts. Model Serving then deploys the model behind a managed, autoscaling endpoint, and AI Gateway sits in front of it to centralize routing, rate limits, fallbacks, and guardrails across every model call. Databricks Apps can host the application that calls the served model, and Agent Bricks covers the additional work of building and governing an agent rather than a direct model call.

Conclusion

Databricks connects the LLM lifecycle by assigning a specific product to each stage instead of asking a team to stitch one together. Lakeflow and Unity Catalog handle governed data, AI Runtime trains and fine-tunes the model, MLflow evaluates it, and Model Serving with AI Gateway puts it into production.