Training a Time-Series Forecasting Model Using Automated Machine Learning
Imagine having the power to predict the unpredictable, to foresee the future of your business, your health, or your environment. What if you could unlock the secrets of time itself? Welcome to the world of time-series forecasting, where machine learning meets magic. Join us to discover how Automated Machine Learning can revolutionize your understanding of the future and uncover the hidden patterns that shape our world. Read on to unlock the secrets of time, and unleash the power of prediction.Unleashing the Potential of AI & Data Science: A quick summary into Microsoft's Tools for students
Explore the transformative power of AI and Data Science through Microsoft's innovative tools, including Azure AI Studio and Azure Machine Learning. Learn how to set up, prepare data, build models, and deploy solutions to revolutionize data analysis.Train a simple Recommendation Engine using the new Azure AI Studio
The AI Studio Odyssey: Embark on a journey to the heart of personalization with our latest guide, “Train a Simple Recommendation Engine using the new Azure AI Studio.” Unlock the secrets of the all-new Azure AI Studio intuitive tools to craft a recommendation system that feels like magic, yet is grounded in data and user preferences. Ready to enchant your audience? Grab some popcorn and read on!Train a Simple Recommendation Engine using Azure Machine Learning Designer
“Unlock the Magic: Train Your AI Wizardry!” Dive into our guide on creating a recommendation engine with Azure Machine Learning Designer. Discover how to weave data spells, conjure personalized suggestions, and make your users feel like they’ve stumbled upon a digital fortune teller. Ready to enchant your audience? Read on!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?
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.
