Anomaly detection plays a vital role in many industries across the globe, such as fraud detection for the financial industry, health monitoring in hospitals, fault detection and operating environment monitoring in the manufacturing, oil and gas, utility, transportation, aviation, and automotive industries.
Multi-language speech transcription was recently introduced into Microsoft Video Indexer at the International Broadcasters Conference (IBC). It is available as a preview capability and customers can already start experiencing it in our portal.
It's exciting to see the PyTorch Community continue to grow and regularly release updated versions of PyTorch! Recent releases improve performance, ONNX export, TorchScript, C++ frontend, JIT, and distributed training. Several new experimental features, such as quantization, have also been introduced.
Built-in Jupyter notebooks for Azure Cosmos DB are now publicly available. Developers, data scientists, engineers and analysts can use the familiar Jupyter notebooks experience to interactively run queries, explore and analyze data, visualize data & build, train, and run machine learning and AI models.
Python support for Azure Functions is now generally available and ready to host your production workloads across data science and machine learning, automated resource management, and more.
Collaborating on data across organizations and integrating it into business decision making is foundational to digital transformation initiatives in organizations. To enable rich data collaboration, a new capability is needed to make sharing data of any size and shape, simple and governed.
This year at Microsoft Build 2019, we announced a slew of new releases as part of Azure Machine Learning service which focused on MLOps. These capabilities help you automate and manage the end-to-end machine learning lifecycle.
With the proliferation of patient information from established and current sources, accompanied with scrupulous regulations, healthcare systems today are gradually shifting towards near real-time data integration.
Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organization’s security and compliance policies. Notebook Virtual Machine (VM), announced in May 2019, resolves these conflicting requirements while simplifying the overall experience for data scientists.
The automated machine learning capability in Azure Machine Learning service allows data scientists, analysts, and developers to build machine learning models with high scalability, efficiency, and productivity all while sustaining model quality.