Saving an sklearn model to blob storage from AzureML Simply put, I have a sklearn model in Azure ML. I want to write the model to a specific blob storage container to save it. I've already authenticated the connection to…
I'm experiencing problems when following the basic instructions from Azure ML docs to work locally on Python notebooks and track them in Azure ML via MLflow. I'm using a conda environment, it's activated and used as kernel in VSC. All…
Using RunConfiguration() class, I used the following way to pass my custom Dockerfile for setting up the environment for the python script rc = RunConfiguration() #rc.environment.use_docker = True rc.docker = DockerConfiguration(use_docker=True) rc.environment.from_dockerfile("webscraping_env", "./Dockerfile") I can see in the config file…
I created a model in Azure ML through AutoML. Then I selected the "best model": , then I selected the job that created that model: , and I finally deployed the model to a real-time endpoint: With Azure ML v1,…
The GUI in Azure Machine Learning for creating datasets is straight forward. But I have a hard time creating it through python code. I'm using the Python 3.8 Azure ML kernel. Here is the code I have but it's running…
I've used Azure AutoML to build and train a classification model. However, instead of deploying the model to a web service or real-time endpoint, I'd like to be able to download the model and run it on my local machine.…
I am trying to setup an Azure ML workspace with Storage Accound behind the Vnet but when trying to upload an sata from Data tab I am getting below error. I have all the necessary setting as describe in the…
1: I created a datastore using azure blob Storage and I selected the table which is in parquet format now i am using that table while data assets creation. 2: I am able to create a dataframe of that data…
I'm using Azure ML jobs to run an experiment using python sdk-v2, and I haven't be able to access into the run logs after the run is completed. I'm not sure what is happening, if I'm missing some permission or…
I am using the mltable library on an AzureML notebook. I can successufully load a local csv file as an mltable: from mltable import from_delimited_files paths = [{'file': "dati_estra_test.csv"}] dati = from_delimited_files(paths) And I can view it as a pandas…