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.
In Craig Kerstiens‘s latest blog post, “A health check playbook for your Postgres database” he emphasizes the need for periodic checks for your Postgres databases to ensure it’s healthy and performing well.
At Microsoft Build 2019 we announced MLOps capabilities in Azure Machine Learning service. MLOps, also known as DevOps for machine learning, is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production of the machine learning (ML) lifecycle.
Digital disruption has created unlimited potential for companies to embrace data as a competitive advantage for their business. As a result, analytics continues to be a key priority for enterprises. When it comes to analytics, customers tell us that they need a solution that provides them with the best price, performance, security, and privacy, as well as a system that can easily deliver powerful insights across the organization. Azure has them covered.