Introduction
Databricks Model Serving enables real-time, batch, and streaming model deployment directly on the Databricks Lakehouse Platform, allowing teams to serve models with low latency, version control, and unified observability—all within a familiar, secure ecosystem. Powered by Databricks’ scalable compute engine, Model Serving supports models trained in any framework (e.g., PyTorch, XGBoost, TensorFlow, Hugging Face Transformers), and integrates seamlessly with Feature Store, Unity Catalog, and Delta Lake.
Key Benefits of Using Databricks Model Serving include:
First-Class MLflow Integration: Enables automatic deployment of models tracked in MLflow with versioning, stage transitions, and lineage—making CI/CD for ML safe and auditable.
Lakehouse-Native Inference: Leverages data, features, and artifacts stored in the Lakehouse for real-time and batch scoring without moving data.
Real-Time REST Endpoints: Offers fully managed HTTP endpoints with auto-scaling, low latency, and integrated security controls (IAM, token auth, Unity Catalog).
GPU and CPU Support: Configurable hardware acceleration for serving deep learning models, transformer-based LLMs, or image classifiers.
Model Monitoring and Metrics: Out-of-the-box tracking of latency, throughput, and input/output logs, with integration into Databricks Observability and MLflow metrics.
Use Cases
Within the Cake platform, Databricks Model Serving is used for:
Real-time prediction APIs for customer segmentation, recommendations, and LLM reranking.
Batch scoring jobs that run on Delta tables using scheduled workflows (via Databricks Workflows or external orchestrators like Airflow or Dagster).
Model lifecycle management, with promotion from staging to production through MLflow stages and approval gates.
Embedding generation and feature transformation, where inference results are stored back into the Lakehouse or exposed via Unity Catalog.
LLM deployment for fine-tuned models, allowing teams to serve internal variants of GPT, BERT, or T5 with full visibility into latency and version drift.
Databricks Model Serving connects natively to the broader Cake stack—via Unity Catalog for governance, Feature Store for consistency, and Spark for scalable downstream pipelines. It also complements evaluation tools like DeepEval, TrustCall, and LangFuse, and integrates into end-to-end workflows involving dbt, MLFlow, and TensorBoard. By adopting Databricks Model Serving, Cake ensures its ML and LLM models are production-grade, governed, and seamlessly integrated into its unified data and compute platform—enabling rapid iteration, safe deployment, and reliable performance across the AI lifecycle.