Data professionals

Customers such as Allscripts, Chevron, J.B. Hunt, and thousands of others are migrating their important workloads to Azure where they find unmatched security.

Learn how to monitor performance and resource utilization on Azure HDInsight by keeping tabs on metrics, such as CPU, memory, and network usage, to better understand how your cluster is handling your workloads and whether you have enough resources to complete the task at hand.

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.

Since the launch of the open source FHIR Server for Azure on GitHub last November, we have been humbled by the tremendously positive response and surge in the use of FHIR in the healthcare community.

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.

One of the largest gatherings of healthcare IT developers will come together on the Microsoft campus next week for HL7 FHIR DevDays, with the goal of advancing the open standard for interoperable health data, called HL7® FHIR® (Fast Healthcare Interoperability Resources, pronounced “fire”).

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?

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.

For businesses today, data is indispensable. Innovative ideas in manufacturing, health care, transportation, and financial industries are often the result of capturing and correlating data from multiple sources.

Improving patient outcomes and reducing healthcare costs depends on healthcare providers such as doctors, nurses, and specialized clinician ability to access a wide range of data at the point of patient care in the form of health records, lab results, and protocols.

Elasticity means services can expand and contract on demand. This means Microsoft Azure customers who are on a pay-as-you-go plan will reap the most benefit out of Azure services.