Machine learning is gaining momentum across a number of industries and scenarios as enterprises look to drive innovation, increase efficiency, and reduce costs. Microsoft Azure Machine Learning empowers developers and data scientists with enterprise-grade capabilities to accelerate the ML lifecycle. There were several advances announced at Microsoft Build 2020, across the following areas: ML for all skills, Enterprise grade MLOps, and responsible ML.
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Data scientists and developers can now access an enhanced notebook editor directly inside Azure Machine Learning studio. New capabilities to create, edit, and collaborate make remote work and sharing easier for data science teams and the notebook is fully compatible with Jupyter.
New reinforcement learning support in Azure Machine Learning enables data scientists to train agents who interact with the real world, such as control systems and game characters. To train agents on Azure Machine Learning, data scientists can use the SDK, studio UI, or command line interface (CLI). Azure Machine Learning simplifies running reinforcement learning at scale on remote compute clusters, including tracking experiment results in Tensorboard and Azure Machine Learning studio UI.
Projects that have a computer-vision component, such as image classification or object detection, generally require labels for thousands of images. Data labeling in Azure Machine learning gives you a central place to create, manage, and monitor labeling projects. Use it to coordinate data, labels, and efficiently manage labeling tasks. The new ML assisted labeling feature helps trigger automatic machine learning models to accelerate the labeling task and is available for image classification and object detection tasks.
To enable secure model training and deployment, Azure Machine Learning provides a strong set of data and networking protection capabilities. These include support for Azure Virtual Networks, dedicated compute hosts and customer managed keys for encryption in transit and at rest. In addition, Microsoft are enabling Private Link for network isolation to access Azure Machine Learning over a private endpoint in your virtual network, so the Azure Machine Learning workspace will not be accessible to the internet. This is critical for many scenarios in regulated industries like financial services, insurance, and healthcare.
Many enterprises have a large corpus of documents and can build cognitive search solutions to search for specific terms and find relevant results to improve productivity. To build an effective solution, often customised models are needed to enrich the search experience. Using Azure Machine Learning, developers can deliver custom search solutions by training and deploying models and now, seamlessly integrating the end points into the Azure Cognitive Search skillset.
The new responsible ML capabilities in Azure Machine Learning empower data scientists and developers to understand ML models, protect people and their data, and control the end-to-end ML process. To learn more, read the responsible ML announcements from Build.
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