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This reference describes the types, paths, and any request payload or query parameters, for each supported Azure Databricks REST API operation. Many reference pages also provide request and response payload examples. Some reference pages also provide examples for calling an Azure Databricks REST API ...
use theMLflow Model Registryto manage and automate the promotion of models towards production.JobsandModel Servingallow hosting models as batch and streaming jobs and as REST endpoints. For more information and examples, see theMLflow for gen AI app and model lifecycleor theMLflow Python API docs...
An MLflowModelis a standard format for packaging machine learning models that can be used in a variety of downstream tools—for example, batch inference on Apache Spark or real-time serving through a REST API. The format defines a convention that lets you save a model in differentflavors(pytho...
I keep getting the following error although the model definitely does exist and version names and model name is correct RestException: NOT_FOUND: The directory being accessed is not found. when calling # Deploy the model to the review app and a model... ...
In addition to these features, there are a host of improvements and bugfixes to the REST API, Python API, tracking UI, and documentation. Theexamples/subdirectory has also been revamped to make it easier to jump in, and examples demonstrating multistep workflows and hyperparameter tuning have ...
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Create a cluster using the DB REST API:The DB REST API is an HTTP API that you can use to manage your DB clusters. To create a cluster using the DB REST API, make a POST request to the/clusters/createendpoint. 21. How can you optimize the performance of your Databricks clusters?