We’re excited to share that Microsoft has been recognized as a Leader in the IDC MarketScape Worldwide Machine Learning Operations (MLOps) Platforms 2022 Vendor Assessment.
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Azure AI is driving innovation and improving experiences for employees, users, and customers in a variety of ways, from increasing workday productivity to promoting inclusion and accessibility. The…
Let’s explore what’s new for Azure Data & AI this month.
Microsoft Azure is the only global public cloud service provider that offers purpose-built AI supercomputers with massively scalable scale-up-and-scale-out IT infrastructure comprised of NVIDIA InfiniBand interconnected NVIDIA Ampere A100 Tensor Core GPUs.
This year at Microsoft Ignite, we explore how organizations can activate AI and automation directly in their business workflows and empower developers to use those same intelligent building blocks to deliver their own differentiated experiences.
Finding scalable solutions for today’s global challenges requires forward-thinking, transformative tools. As environmental, economic, and public health concerns mount, Microsoft Azure is addressing these challenges head on with high-performance computing (HPC), AI, and machine learning.
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.
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.
The capacity of a system to adjust to changes by adding or removing resources to meet demand is known as scalability. Here are some tests to check the scalability of your MLOps model.
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.