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.
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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.
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.
Microsoft is committed to the responsible advancement of AI to enable every person and organization to achieve more. Over the last few months, we have talked about advancements in our Azure infrastructure, Cognitive Services, and Azure Machine Learning to make Azure better at supporting the AI needs of all our customers, regardless of their scale.
Over the past few years, there has been an increasingly steady drumbeat for the need to diversify and open the telecommunications supply chain. A key part of this supply chain that can be diversified is radio access network (RAN), where operators have typically spent most of their investments in network infrastructure. To address the need for diversification, groups such as the O-RAN alliance have formed to open up RAN capabilities.
As AI becomes more deeply embedded in our everyday lives, it is incumbent upon all of us to be thoughtful and responsible in how we apply it to benefit people and society. Join our digital event, Put Responsible AI into Practice, to learn more about these updates, including new guidelines for product leaders and a Responsible AI dashboard for data scientists and developers.
AI is transforming business and the world. However, AI models learn from the data. Biases that exist in society will exist in the models. Human judgment must be the overriding factor, ensuring that AI models benefit and are inclusive of everyone. Equally important, AI must inspire trust in customers that their data is being used appropriately. These are key reasons that responsible approaches to AI are so critical, and you can learn how to put responsible AI into practice.
A growing number of organizations are taking advantage of machine learning to increase efficiency, enhance customer experiences and drive innovation. Azure Machine Learning is the enterprise-grade service to build and deploy models faster and accelerate the machine learning lifecycle.
Today, AI and machine learning are enabling data-driven organizations to accelerate their journey to insights and decisions. With all the latest advancements, AI is no longer limited to only those with deep expertise or a cache of data scientists, and many organizations can now adopt AI and machine learning for better competitive advantage. Customers with analytics practices looking to adopt machine learning can read this report to get started.
Today, Azure announces the general availability of the Azure ND A100 v4 Cloud GPU instances—powered by NVIDIA A100 Tensor Core GPUs—achieving leadership-class supercomputing scalability in a public cloud. For demanding customers chasing the next frontier of AI and high-performance computing (HPC), scalability is the key to unlocking improved total cost of ownership and time-to-solution.