AutoML - Featurisation option removed in UI update
Hi, When using the no-code AutoML UI to create models there was an option to exclude fields from the models training (the primary key, for example). However since the UI update this seems to have gone, and the documentation still gives guidance for the old UI. I'd like to know if there is another way I can achieve excluding fields from the training data that will still be required to pass to the model once deployed, using the UI? I assume it is still working via the SDK (am yet to check), but the UI offered a good introduction to modelling for users with low code knowledge. Old UI from documentation (note 'Included' slider): New UI missing the 'Included' slider: Thanks in advance. EDIT: I have had confirmation from MS support that the feature was removed in the upgrade to v2, and advised to featurise fully initially. Not ideal as there isn't a way to explicitly set/exclude the primary key now, but hopefully it never assigns it any importance.
Azure ML - Get Started - Create Compensation Prediction
Hi Guys, Super new here. Is there anyway to use Azure ML for free in an enterprise environment. I want to play around with some actual employee compensation data. I want to create a model, and I have no idea how to do this, that would allow an employee enter things like a persons specialty, years of experience, if they have specific training, if its a highly sought after postition. I can feed the model actual data with these data tagas but is there a way to play or create something for free..especially since I am new and do not know what I am doing. Any advice or help would be awesome. I would ultimately want to deploy this model to a powerapp that would sit in sharepoint. Thanks in advance for reading and responding.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
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.
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?Can we use Azure Machine Learning on a Azure Analysis Services?
Hello, it's my first post ever since i was hired as Data Scientist after graduating this month. Of course, i have no experience in any cloud microsoft techonologies (yet) and ask for forgiveness in advance if I mix concepts incorrectly. Also, i tried to search similar answers in the forum but sadly found nothing. In our business, we deployed an Azure Analysis Services with our data models ( i think it's called datalake). These models are used most of them for Reporting in Power BI. Right now, we would like to explore more deep types of analysis using machine learning techniques in Azure Machine Learning. The basic problem is how do i acces to the information in the tabular models in Azure Analysis Services from Azure Machine learning? Is this new platform (Azure ML) able to do that easily, without any trick? For example, we tried to make some querys (to Azure Analysis Services) from our locals, using python and pyodbc library. This never worked and there's no information (at least i've found) in the internet. The reason to use python to make querys it's for practical reasons. You make the query, and you call still work on the same notebook without downloading anything externaly, then throw X machine learning algorithm to do Classification, Regression... in the data selected in the query to make a Exploratory Data Analysis. So our idea, would be to replace this locals machines for something in the cloud which i hope have a direct implementation to work with that and also better hardware. Am i wrong, right...? Do you have any suggestion of how we should do things? Please, correct me! Thank you for your answer!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.
