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Tag: Machine Learning

Azure empowers easy-to-use, high-performance, and hyperscale model training using DeepSpeed

Large-scale transformer-based deep learning models trained on large amounts of data have shown great results in recent years in several cognitive tasks and are behind new products and features that augment human capabilities. Azure Machine Learning (AzureML) brings large fleets of the latest GPUs powered by the InfiniBand interconnect to tackle large-scale AI training. Source…

MLOps Blog Series Part 4: Testing security of secure machine learning systems using MLOps

The growing adoption of data-driven and machine learning-based solutions is driving the need for businesses to handle growing workloads, exposing them to extra levels of complexities and vulnerabilities. Here are some key approaches and tests for securing your machine learning systems against attacks with Azure Machine Learning using MLOps. Source…

MLOPs Blog Series Part 2: Testing robustness of secure machine learning systems using machine learning ops

Robustness is the ability of a closed-loop system to tolerate perturbations or anomalies while system parameters are varied over a wide range. There are three essential tests to ensure that the machine learning system is robust in the production environments: unit tests, data and model testing, and integration testing. Source…

Discover how you can innovate anywhere with Azure Arc

Welcome to Azure Hybrid, Multicloud, and Edge Day—please join us for the digital event. Today, we’re sharing how Azure Arc extends Azure platform capabilities to datacenters, edge, and multicloud environments through an impactful, 90-minute lineup of keynotes, breakouts, and technical sessions available live and on demand. Source…

MLOps Blog Series Part 1: The art of testing machine learning systems using MLOps

Testing is an important exercise in the life cycle of developing a machine learning system to assure high-quality operations. In this blog, we will look at testing machine learning systems from a Machine Learning Operations (MLOps) perspective and learn about good case practices and a testing framework that you can use to build robust, scalable, and secure machine learning systems. Source…

Introducing the Microsoft Intelligent Data Platform

It’s clear that the fragmentation which exists today between databases, analytics, and governance products must be addressed. To help organizations unlock these new capabilities, we shared several exciting announcements today at Microsoft Build that demonstrate our continued innovation and investment in the data products our customers have come to know and trust. Source…

Feathr: LinkedIn’s feature store is now available on Azure

With the advance of AI and machine learning, companies start to use complex machine learning pipelines in various applications, such as recommendation systems, fraud detection, and more. These complex systems usually require hundreds to thousands of features to support time-sensitive business applications, and the feature pipelines are maintained by different team members across various business groups. Source…

Improving the cloud for telcos: Updates of Microsoft’s acquisition of AT&T’s Network Cloud

In June 2021, Microsoft and AT&T reached a major milestone when we announced an industry-first collaboration to adopt Microsoft cloud technology for AT&T’s 5G core network workloads. Since then, we have had requests from many operators, partners, and customers to share more details. This blog is intended to do just that. Source…