azure-machine-learning-service

How to register a local model of 900mb in Azure Machine Learning Service with CLI or SDK

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问题 I have a 900mb model and a 9mb model. I am using the latest version of the Azure ML CLI to register it in my workspace with this command: az ml model register -n "rj-model" --model-path "models\model_v1.bin -t "model-deployment\model.json" The 9mb file uploads successfully however the 900 mb file times out with this error: {'Azure-cli-ml Version': '1.0.60.1', 'Error': AzureHttpError('Operation could not be completed within the specified time. ErrorCode: OperationTimedOut\n<?xml version="1.0"

Calculating helping columns on R script on azure machine learning, so they could be later added to tabular model

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问题 So I am creating flow for imorting data, so some aggragate columns could be uploaded to Azure Sql database and later on tabular model. Now I would to describe the flow, so someone could tell about it, pros and cons. At this stage of development the flow is: 1. User imports CSV file to my web service (in ASP.NET CORE 2.1) to Azure Sql database, for importing I am using Sql Bulk Library in .NET Core. The webservice and database will be located on the server in azure. Some of the data

Registering and downloading a fastText .bin model fails with Azure Machine Learning Service

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问题 I have a simple RegisterModel.py script that uses the Azure ML Service SDK to register a fastText .bin model. This completes successfully and I can see the model in the Azure Portal UI (I cannot see what model files are in it). I then want to download the model (DownloadModel.py) and use it (for testing purposes), however it throws an error on the model.download method ( tarfile.ReadError: file could not be opened successfully ) and makes a 0 byte rjtestmodel8.tar.gz file. I then use the

Difference between Azure ML and Azure ML experimentation

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问题 I am new to Azure ML. I am having some doubts .Could anyone please clarify my doubts listed below. What is the difference between Azure ML service Azure ML experimentation service. What is the difference between Azure ML workbench and Azure ML Studio. I want to use azure ML Experimentation service for building few models and creating web API's. Is it possible to do the same with ML studio. And also ML Experimentation service requires me to have a docker for windows installed for creating web

Difference between Azure ML and Azure ML experimentation

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I am new to Azure ML. I am having some doubts .Could anyone please clarify my doubts listed below. What is the difference between Azure ML service Azure ML experimentation service. What is the difference between Azure ML workbench and Azure ML Studio. I want to use azure ML Experimentation service for building few models and creating web API's. Is it possible to do the same with ML studio. And also ML Experimentation service requires me to have a docker for windows installed for creating web services. Can i create web services without using docker? The AML Experimentation is one of our many

Unable to register an ONNX model in azure machine learning service workspace

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I was trying to register an ONNX model to Azure Machine Learning service workspace in two different ways, but I am getting errors I couldn't solve. First method: Via Jupyter Notebook and python Script model = Model.register(model_path = MODEL_FILENAME, model_name = "MyONNXmodel", tags = {"onnx":"V0"}, description = "test", workspace = ws) The error is : HttpOperationError: Operation returned an invalid status code 'Service invocation failed!Request: GET https://cert-westeurope.experiments.azureml.net/rp/workspaces ' Second method: Via Azure Portal Anyone can help please? error 413 means the

Why does my ML model deployment in Azure Container Instance still fail?

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问题 I am using Azure Machine Learning Service to deploy a ML model as web service. I registered a model and now would like to deploy it as an ACI web service as in the guide. To do so I define from azureml.core.webservice import Webservice, AciWebservice from azureml.core.image import ContainerImage aciconfig = AciWebservice.deploy_configuration(cpu_cores=4, memory_gb=32, tags={"data": "text", "method" : "NB"}, description='Predict something') and image_config = ContainerImage.image_configuration