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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
Enabling Resilient Machine Learning Systems
Article by Francesca Lazzeri and Bea Stollnitz of Microsoft. Resilient machine learning systems are fast, accurate, and flexible. They assist you in your day-to-day tasks for maximum efficiency, they leverage the latest software and hardware for the fastest performance, and they guide you through complex tasks... Read more
Evaluate ML Models with Azure Machine Learning’s Responsible AI Insights
In December 2021, we introduced the Responsible AI dashboard, a comprehensive experience bringing together several mature Responsible AI tools in the areas of data explorer (to proactively identify whether there is sufficient data representation for the variety of data subgroups), fairness assessment (to assess and identify... Read more
How to Connect Azure Synapse Analytics to Power BI 
The first question on your mind, well that is if you are unfamiliar with Azure Synapse Analytics is probably, what is Azure Synapse? Azure Synapse Analytics is an analytics service that enables you to bring together data integration, warehousing and big data analytics. Using Azure Synapse... Read more
MLOps V2 Solution Accelerator – Unifying MLOps at Microsoft
Article by Scott Donohoo and Setu Chokshi of Microsoft. MLOps means different things to different people, however, the fundamental essence of MLOps is to deliver models into productions faster with a consistent, repeatable, and reliable approach. Machine Learning Operations (MLOps) is key to accelerating how data... Read more
Many Models Training with Hyperparameter Optimization
This article presents an approach for you to train multiple machine learning models, optimizing the hyperparameters of each model in an automated way with Azure Machine Learning. Before getting into the part where I explain how to do this, let’s first get a better understanding of... Read more
How to Choose the Best GPU Optimized VM Sizes for Your Project on Azure
A common problem that data scientists face when training and deploying their machine learning models is the choice of the right type and size of hardware. Migrating machine learning tasks on the Cloud significantly simplified the data scientist’s job, who now just needs to login into... Read more