![]() Optional scoringResult = scoringRequest.flatMap(req -> getRequestBody(client, req)) Optional scoringRequest = authKey.flatMap(key -> Optional.of(scoringRequest(key, scoringUri, dataToBeScored))) Optional authKey = authBody.flatMap(body -> Optional.of(omJson(body, AuthenticationBody.class).access_token) Optional authBody = getRequestBody(client, tokenAuthenticationRequest) HttpRequest tokenAuthenticationRequest = tokenAuthenticationRequest(tenantId, clientId, clientSecret, resourceManagerUrl) HttpClient client = HttpClient.newBuilder().build() Response = requests.post(published_pipeline1.endpoint, Getting such a token is described in the AzureCliAuthentication class reference and in the Authentication in Azure Machine Learning notebook. To invoke the run of the preceding pipeline, you need an Azure Active Directory authentication header token. If you are using Azure role-based access control (Azure RBAC) to manage access to your pipeline, set the permissions for your pipeline scenario (training or scoring). This endpoint enables "managed repeatability" in batch scoring and retraining scenarios. With the pipeline endpoint, you can trigger a run of the pipeline from any external systems, including non-Python clients. Pipeline ID is the unique identified of the published pipeline.Īll published pipelines have a REST endpoint. published_pipeline1 = pipeline_run1.publish_pipeline(ĭescription="My Published Pipeline Description",Īfter you publish your pipeline, you can check it in the UI. Publish this pipeline that will accept a parameter when invoked. from import PipelineParameterĪdd this PipelineParameter object as a parameter to any of the steps in the pipeline as follows: compareStep = PythonScriptStep(Īrguments=, To create a pipeline parameter, use a PipelineParameter object with a default value. For the REST endpoint of an already published pipeline to accept parameters, you must configure your pipeline to use PipelineParameter objects for the arguments that will vary. Once you have a pipeline up and running, you can publish a pipeline so that it runs with different inputs. For other options, see Create and run machine learning pipelines with Azure Machine Learning SDK PrerequisitesĬreate an Azure Machine Learning workspace to hold all your pipeline resourcesĬonfigure your development environment to install the Azure Machine Learning SDK, or use an Azure Machine Learning compute instance with the SDK already installedĬreate and run a machine learning pipeline, such as by following Tutorial: Build an Azure Machine Learning pipeline for batch scoring. You can also version pipelines, allowing customers to use the current model while you're working on a new version. One benefit of pipelines is increased collaboration. Machine learning pipelines are reusable workflows for machine learning tasks. This article will show you how to share a machine learning pipeline with your colleagues or customers.
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