Azure Communication Services in Healthcare Update – HIMSS & BUILD 2023
AI took center stage at HIMSS23 with announcements from Andor Health, Epic, and Nuance. Amy Berk, Dir. of Population Health at Microsoft states, “Azure Communication Services amplifies virtual health by integrating communication into the real-time virtual environment; enabling the patient in multiple ways thereby enhancing and personalizing the patient-provider relationship at the point of care.”Empowering Snowflake users with Azure Machine Learning
Azure Machine Learning (Azure ML) offers a preferred platform for Snowflake, a cloud-based data warehouse, that is increasingly becoming the go-to choice for many organizations to store their data. Data scientists from organizations that have adopted Snowflake as their data warehouse solution can now explore Azure ML capabilities without relying on third-party libraries or engaging data engineering teams. Now, with a seamless and native integration between Snowflake and Azure Machine Learning, data scientists can import their data from Snowflake to Azure ML with a single command and kick-start their machine learning projects. We are thrilled to announce the public preview of Azure Machine Learning (Azure ML) data import CLI & SDK which is designed to bring data effortlessly from data repositories that are not part of Azure platform for training in Azure ML. This includes databases like Snowflake and cloud storage services like AWS S3. This blog post will outline the advantages and steps to get started with Azure Machine Learning for Snowflake users without any external dependencies. Advantages of Snowflake and Azure Machine Learning Integration Enhanced Collaboration: This integration empowers data scientists to directly import data from Snowflake, eliminating the need for constant communication with data engineering teams. Time Efficiency: By removing the need for third-party libraries or additional data pipeline development, the data scientists can save time and focus on developing their machine learning models. Simplified Workflow: Leveraging native connectivity between Snowflake and Azure Machine Learning results in a more streamlined and user-friendly workflow. Flexibility: Leveraging schedules or on-demand options, data scientists can decide when and what data needs to be imported. With certain configurations the data expirations can also be managed, providing them complete flexibility on the datasets. Traceability: Each import, whether scheduled or not, creates a unique version of the dataset which is in turn used in training jobs, giving data scientists the required traceability in scenarios that need retraining or for model audits. How to get started? A connection is where it all begins, this is where the endpoint details of the Snowflake instance including the server, database, warehouse and role information is entered as target along with valid the credentials to access data. Typically, an admin is the one who would create a connection. It is easy for a data scientist to use an existing connection if the query to pull the required data that is intended to be used in training is known. In one single step, one can import data and register it as a Azure ML data asset that can be readily referenced in training jobs. If the scenario demands to import data on a schedule, one can use popular cron or recurrence patterns to define the frequency of import. We are also excited to introduce the public preview of lifecycle management on Azure ML managed datastore (a “Hosted On Behalf Of/HOBO datastore). This offering from Azure ML is exclusively for data import scenarios available on CLI and SDK. On choosing HOBO datastore as their preferred destination to import data, one gets the capability of lifecycle management or as we call it “auto delete settings” on the imported data assets. A policy to automatically delete an imported data asset if unused for 30 days by any job is set on every imported data asset in the AzureML managed datastore. All that one has to do is to set “azureml://datastores/workspacemanagedstore” as the path when defining their import as shown in the snippet below and rest will be handled by AzureML. Once the data is imported, one can update the auto delete settings to lengthen or shorten the time duration or even change the condition to be based on created time instead of last used time using CLI or SDK commands. Quick recap – Customers having data in Snowflake can now utilize the power of Azure ML for training directly from our platform. They can import data on-demand or on a schedule and can also set “auto-delete” policies to manage their imported data in Azure ML managed datastore from cost and compliance point of view. Try it for yourself – To get started with Azure Machine Learning data import, please visit Azure ML documentation and GitHub repo, where you can find detailed instructions to setup connections for external sources in Azure ML workspace , and train or deploy models with a variety of Azure ML examples. Learn More – To stay updated on Azure Machine Learning announcements, watch our breakout sessions from Microsoft Build. Build and maintain your company Copilot with Azure Machine Learning and GPT-4 Practical deep-dive into machine learning techniques and MLOps Building and using AI models responsiblyOpen at Microsoft - Dapr
At Microsoft, our goal is to empower all developers to be successful at building any application, using any language, on any platform. We are committed to building Open, flexible technology that makes it easy for any developer to build adaptable and dynamic applications that run on the cloud and edge and embraces the diversity of languages and developer frameworks. Microsoft is an active member and core contributor to open source ecosystems and projects such as the Linux Foundation, Eclipse Foundation, Python Software Foundation, and Rust Foundation. GitHub is home to and the place for anyone from anywhere to build anything, we continue to strengthen our commitment to developer freedom, openness, and innovation. As one of the top open source collaborators in the world, Microsoft is committed to bringing our engineers and innovation to help foster a healthy open source ecosystem that our customers, developers, and partners can use, modify, improve, and build upon. We believe that open collaboration often allows communities to achieve more than their members can do alone and accelerate innovation. We foster active communities around our own home-grown projects like VS Code, TypeScript, and .NET. One of the new ways we are continuing to engage and excite our developer audience is through the Open at Microsoft show series. The Open at Microsoft show is committed to showcasing and highlighting open source projects that are both Microsoft born and also featured collaborations. The Open at Microsoft series is a weekly show that features open source projects and highlights how Microsoft is continuing their efforts to evolve the open source community to be an even better project and making it more useful for the Developer community. For today’s blog, we are going to be featuring the Dapr (Distributed Application Runtime) project. Dapr or Distributed Application Runtime, is a new way to build modern distributed applications. What started as a prototype has evolved into a highly successful open-source project. The Dapr project was born at Microsoft and donated to the Cloud Native Computing Foundation as part of an incubation project. Dapr helps you write flexible and secure microservices that leverages industry proven best practices to build connected distributed applications faster. By letting Dapr’s sidecar take care of the complex challenges such as service discovery, message broker integration, encryption, observability, and secret management, you can focus on business logic and keep your code simple. At its core, it is focused on making microservice and distributed app developers more productive through utilization of building block APIs and tools to keep your platform agnostic. Learn from our experts Aaron Crawfis and Paul Yuknewicz as they dive deep into the infrastructure of Dapr on the Open at Microsoft show. Find the episode here You can get actively involved in the Dapr community through participating in discussions at community engagements or writing a blog entry. Learn more about how to get involved with Mark Fussel and Aaron Crawfis on this next episode on the Open at Microsoft show series. The episode can also be viewed here on the Microsoft Learn TV platform. Be on the lookout for the next featured open source episode from the Open at Microsoft show series and know how you can contribute back to the ecosystem. To get new updates of episode releases be sure to subscribe to our YouTube page here. In the meantime, Join us this year in Seattle for Microsoft Build! Learn from Microsoft’s leading experts as they share key announcements on Azure, GitHub Copilot, GPT-4, and more. Also, Dive deeper on topics covered in breakouts and keynotes in small-group discussions. For more information, register here.
