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Highlighting Microsoft’s Data Science and AI Learning Paths
We are all familiar with Microsoft and Microsoft Azure, but have you explored their wide range of learning paths, available for free? Check out a few of them below. MLOps End-to-end Machine Learning Operations (MLOps) with Azure Machine Learning In this learning path, you’ll learn how... Read more
Generative AI in Azure Machine Learning: Operationalizing App Development for AI Transformation
In the age of generative AI, leaders find themselves at an intersection of innovation and purpose. The question that reverberates though boardrooms and data science conferences is this: How can this new constellation of cutting-edge technologies be harnessed to advance organizational objectives with creativity and business... Read more
Machine Learning Operations (MLOPs) with Azure Machine Learning
Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. The data... Read more
Announcing Microsoft Azure’s New Tutorial on Deep Learning and NLP
Here at ODSC, we couldn’t be more excited to announce Microsoft Azure’s tutorial series on Deep Learning and NLP, now available for free on Ai+. This course series was created by a team of experts from the Microsoft community, who have brought their knowledge and experience... Read more
Debug Object Detection Models with the Responsible AI Dashboard
At Microsoft Build 2023, we announced support for text and image data in the Azure Machine Learning responsible AI dashboard in preview. This blog will focus on the dashboard’s new vision insights capabilities, supporting debugging capabilities for object detection models. We’ll dive into a text-based scenario... Read more
Using Azure ML to Train a Serengeti Data Model for Animal Identification
Article on Azure ML by Bethany Jepchumba and Josh Ndemenge of Microsoft In this article, I will cover how you can train a model using Notebooks in Azure Machine Learning Studio. To get the data, you will need to follow the instructions in the article: Create... Read more
Distributed training with PyTorch and Azure ML
By Beatriz Stollnitz, Principal Cloud Advocate at Microsoft Suppose you have a very large PyTorch model, and you’ve already tried many common tricks to speed up training: you optimized your code, you moved training to the cloud and selected a fast GPU VM, you installed software packages that... Read more
Faster Training and Inference Using the Azure Container for PyTorch in Azure ML
By Beatriz Stollnitz, Principal Cloud Advocate at Microsoft If you’ve ever wished that you could speed up the training of a large PyTorch model, then this post is for you! The Azure ML team has recently released the public preview of a new curated environment that... Read more
Training Your PyTorch Model Using Components and Pipelines in Azure ML
By Beatriz Stollnitz, Principal Cloud Advocate at Microsoft In this post, we’ll explore how you can take your PyTorch model training to the next level, using Azure ML. In particular, we’ll see how you can split your training code into multiple steps that can be easily... Read more
Training and Deploying Your PyTorch Model in the Cloud with Azure ML
By Beatriz Stollnitz, Principal Cloud Advocate at Microsoft You’ve been training your PyTorch models on your machine, and getting by just fine. Why would you want to train and deploy them in the cloud? Training in the cloud will allow you to handle larger ML models... Read more