Customers such as Allscripts, Chevron, J.B. Hunt, and thousands of others are migrating their important workloads to Azure where they find unmatched security. While understanding cloud security is initially a concern to many, after digging in, customers often tell us the security posture they can set up within Azure is easier to implement and far more comprehensive than what they can provide for in other environments.
When data scientists work on building a machine learning model, their experimentation often produces lots of metadata: metrics of models you tested, actual model files, as well as artifacts such as plots or log files. They often try different models and parameters, for example random forests of varying depth, linear models with different regularization rates, or deep learning models with different architectures trained using different learning rates.
Throughout our Internet of Things (IoT) journey we’ve seen solutions evolve from device-centric models, to spatially-aware solutions that provide real-world context.
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.
Build more accurate forecasts with the release of capabilities in automated machine learning. Have scenarios that require have gaps in training data or need to apply contextual data to improve your forecast or need to apply lags to your features? Learn more about the new capabilities that can assist you.
Azure Cognitive Services provides Text Analytics APIs that simplify extracting information from text data using natural language processing and machine learning. These APIs wrap pre-built language processing capabilities, for example, sentiment analysis, key phrase extraction, entity recognition, and language detection.
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.
During Microsoft Build we announced the preview of the visual interface for Azure Machine Learning service. This new drag-and-drop workflow capability in Azure Machine Learning service simplifies the process of building, testing, and deploying machine learning models for customers who prefer a visual experience to a coding experience.
Conversational experiences have become the norm, whether you’re looking to track a package or to find out a store’s hours of operation. At Microsoft Build 2019, we highlighted a few customers who are building such conversational experiences using the Microsoft Bot Framework and Azure Bot Service to transform their customer experience.
Customers across industries including healthcare, legal, media, and manufacturing are looking for new solutions to solve business challenges with AI, including knowledge mining with Azure Search.