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Databricks
 Contents 
Databricks connector for the SQLDatabase Chain
Databricks-managed MLflow integrates with LangChain
Databricks as an LLM provider
Databricks Dolly
Databricks#
The Databricks Lakehouse Platform unifies data, analytics, and AI on one platform.
Databricks embraces the LangChain ecosystem in various ways:
Databricks connector for the SQLDatabase Chain: SQLDatabase.from_databricks() provides an easy way to query your data on Databricks through LangChain
Databricks-managed MLflow integrates with LangChain: Tracking and serving LangChain applications with fewer steps
Databricks as an LLM provider: Deploy your fine-tuned LLMs on Databricks via serving endpoints or cluster driver proxy apps, and query it as langchain.llms.Databricks
Databricks Dolly: Databricks open-sourced Dolly which allows for commercial use, and can be accessed through the Hugging Face Hub
Databricks connector for the SQLDatabase Chain#
You can connect to Databricks runtimes and Databricks SQL using the SQLDatabase wrapper of LangChain. See the notebook Connect to Databricks for details.
Databricks-managed MLflow integrates with LangChain#
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. See the notebook MLflow Callback Handler for details about MLflow’s integration with LangChain.
Databricks provides a fully managed and hosted version of MLflow integrated with enterprise security features, high availability, and other Databricks workspace features such as experiment and run management and notebook revision capture. MLflow on Databricks offers an integrated experience for tracking and securing machine learning model training runs and running machine learning projects. See MLflow guide for more details.
rtdocs_stable/api.python.langchain.com/en/stable/integrations/databricks.html