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- Databricks の MLflow - Azure Databricks | Microsoft Learn
Databricks の MLflow について説明します。 MLflow は、エージェント、LLM、ML モデル向けの最大のオープン ソース AI エンジニアリング プラットフォームです。
- Create custom model serving endpoints - Azure Databricks
This article describes how to create model serving endpoints that serve custom models using Databricks Model Serving Model Serving provides the following options for serving endpoint creation: The Serving UI REST API MLflow Deployments SDK For creating endpoints that serve generative AI models, see Create foundation model serving endpoints
- Tutorial: Evaluate and improve a GenAI application - Azure Databricks
Learn how to systematically evaluate new versions of your GenAI application in MLflow, assess quality after changes, detect regressions, and iteratively improve your app's quality using `mlflow genai evaluate()`
- MLflow API reference - Databricks on AWS
MLflow API reference The open-source MLflow REST API allows you to create, list, and get experiments and runs, and allows you to log parameters, metrics, and artifacts The Databricks Runtime for Machine Learning provides a managed version of the MLflow server, which includes experiment tracking and the Model Registry
- Build a multi-agent system on Databricks Apps - Azure Databricks
Build a multi-agent orchestrator on Databricks Apps that routes requests to other agents, Genie spaces, and serving endpoints
- MLflow on Databricks: Benefits, Capabilities Quick Tutorial
Discover how to use managed MLflow on Databricks to simplify machine learning lifecycle management with enhanced dependability, security, and scalability
- Tutorial: Create external model endpoints to query OpenAI models . . .
This article provides step-by-step instructions for configuring and querying an external model endpoint that serves OpenAI models for completions, chat, and embeddings using the MLflow Deployments SDK Learn more about external models If you prefer to use the Serving UI to accomplish this task, see Create an external model serving endpoint
- Azure Databricks
On Databricks, Managed MLflow provides a managed version of MLflow with enterprise-grade reliability and security at scale, seamless integrations with the Databricks Machine Learning Runtime, Feature Store, and Serverless Real-Time Inference
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