ml studio

5 Topics

Azure ML Studio - Attached Compute Challenges
Hello community, I'm new to ML services and have been exploring the ML Studio the last while to understand it better from an infrastructure point of view. I understand that I should be able to attach an existing VM (Ubuntu) running in my Azure environment, and use this as a compute resource in the ML Studio. I've come across two challenges, and I would appreciate your help. I'm sure perhaps I am just missing something small. Firstly, I would like to connect to my virtual machine over a private endpoint. What I have tried is to create the private endpoint to my VM following the online guidance (https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-private-link?view=azureml-api-2&tabs=azure-portal). Both the VM and the endpoints are on the same subnet on the same vNet, yet, it is unable to attach the compute. It seems to still default to the public IP of the VM, which is not what I am after. I have the SSH port configured to port 22 still, and I have tried several options on my NSG to configure the source and destination information (Service Tags, IP address, etc.), but with no luck. Am I missing something? Is attaching an existing VM for compute over a private endpoint a supported configuration, or does the private endpoint only support compute created out of the ML Studio compute section? Secondly, if I forget about the private endpoint and attach the VM directly over internet (not desired, obviously), it is not presented to me as a compute option when I try to run my Jupyter Notebook. I only have "Azure Machine Learning Serverless Spark" as a compute option, or any compute that was indeed created through the ML Studio. I don't have the option to select the existing VM that was attached from Azure. Again, is there a fundamental step or limitation that I am overlooking? Thanks in advance
Solved
SebastiaanR
Jun 03, 2025 Place Microsoft Foundry Discussions
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Tech Minutes Video - Project Trove
This post is Authored by Trinh Duong, Christian Liensberger and Giampaolo Battaglia Office of the CTO Team & AI/Innovation at Microsoft We recently launched the Innovation Tech Minutes series, which are short, snackable informative tidbits from Microsoft researchers, developers and engineers all around the world on some of the latest and future technologies. In our latest episode, Christian Liensberger, Principal Program Manager and Advisor to Microsoft’s CTO shares new insights into Project Trove - a crowdsourcing marketplace where you can gather high-quality images for your AI models. Images are responsibly sourced from regular individuals and adhere to a rigid licensing and privacy framework, resulting in a more responsible data collection platform. In this Tech Minutes video, Christian shares the advantages of Trove, and also provides a walkthrough of Trove Web App from an AI Developer standpoint (selecting the right images for your model training), as well as showing how photo takers can upload their images through the Trove App on Android. Watch the Tech Minutes video Happy viewing & happy end of year! Trinh, Christian and Giampaolo
Giampaolo Battaglia
Mar 30, 2023 Place Microsoft Foundry Discussions
1.5KViews
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Jumping from Google's Teachable Machine to Azure. Help
I've been using Google's Teachable Machine for experiments for months, using two classes of images to train for recognition. I now need to switch the data source to tabular data (TM doesn't support this), and feel as though I've walked into Costco, Home Depot and Walgreens combined, with Azure. I've reviewed libraries of demos at studio.azureml.net and signed up for something else related to Azure, but, beyond uploading data, I have yet to find a way to replicate the workflow and simplicity of setup Teachable Machine offered. Any guidance is appreciated as (now knowing 9 computer programming languages) I'm not keen on learning yet another "ecosystem" over the course of X months.
InspireMe
Jun 12, 2021 Place Microsoft Foundry Discussions
1.3KViews
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Azure ML Inference Cluster - AKS with Private IP
I have an AKS cluster in a VNET/Subnet. My AKS is linked to AzureML. I successfully deployed an Azure ML service to that AKS. However, I see that the azureml-fe service is responding to a public IP and not a private IP from my VNET/Subnet. How can I make it so my AzureML inference service is exposed with a private IP?
aferland
Mar 19, 2021 Place Microsoft Foundry Discussions
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common pitfall of using data bricks with pandas and not spark
Hi Team, I just want to understand what could be the common pitfall of using Pandas on Databricks instead of Spark. There are certain factors on which we have decided to go with Databricks instead of Azure AI platform (jupyter notebook). Experiment tracking using ML-ops Hyperparameter tuning with Spark trails which helps with parallelization I just wanted to understand that what could possibly go wrong if we train model on Databricks by just using pandas & sklearn . Deployment: we will deploy final model offline, will different env will cause an issue ? Cost: is AI platform supports points mentioned above, experiment tracking & parallel hyperparameter tuning Ease of use other advantage offered by AI platform (ex: automatic hyperparameter tuning) I am new to Azure service, It will be really helpful if you can share detail answer of above points with your preference. (what you would have chosen and why?) Thanks in Advance.
Rohitshah1990
Nov 09, 2020 Place Microsoft Foundry Discussions
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