In a world where data volume, variety, and type are exponentially growing, organizations need to collaborate with data of any size and shape. In many cases data is at its most powerful when it can be shared and combined with data that resides outside organizational boundaries with business partners and third parties. For customers, sharing this data in a simple and governed way is challenging. Common data sharing approaches using file transfer protocol (FTP) or web APIs tend to be bespoke development and require infrastructure to manage. These tools do not provide the security or governance required to meet enterprise standards, and they often are not suitable for sharing large datasets. To enable enterprise collaboration, we are excited to unveil Azure Data Share Preview, a new data service for sharing data across organizations.
Simple and safe data sharing
Data professionals in the enterprise can now use Azure Data Share to easily and safely share big data with external organizations in Azure Blob Storage and Azure Data Lake Storage. New services will continue to come online. As a fully managed Azure service, Azure Data Share does not require infrastructure to set up and it scales to meet big data sharing demands.
This blog was co-authored by Jordan Edwards, Senior Program Manager, Azure Machine Learning
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.
Historically, Azure Machine Learning service’s management plane has been via its Python SDK. To make our service more accessible to IT and app development customers unfamiliar with Python, we have delivered an extension to the Azure CLI focused on interacting with Azure Machine Learning.
While it’s not a replacement for the Azure Machine Learning service Python SDK, it is a complimentary tool that is optimized to handle highly parameterized tasks which suit themselves well to automation. With this new CLI, you can easily perform a variety of automated tasks against the machine learning workspace, including:
Datastore management Compute target management Experiment submission and job management Model registration and deployment
Combining these commands enables you to train, register their model, package it, and deploy your model as an API. To help you quickly get started with MLOps, we have also released a predefined template in Azure Pipelines. This template allows you
This blog was co-authored by Shweta Mishra, Senior Solutions Architect, CitiusTech and Vinil Menon, Chief Technology Officer, CitiusTech
CitiusTech is a specialist provider of healthcare technology services which helps its customers to accelerate innovation in healthcare. CitiusTech used Azure Cosmos DB to simplify the real-time collection and movement of healthcare data from variety of sources in a secured manner. 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. To realize such performance, healthcare systems not only need to have low latency and high availability, but should also be highly responsive. Furthermore, they need to scale effectively to manage the inflow of high speed, large volumes of healthcare data.
The rise of Internet of Things (IoT) has enabled ordinary medical devices, wearables, traditional hospital deployed medical equipment to collect and share data. Within a wide area network (WAN), there are well defined standards and protocols, but with the ever increasing number of devices getting connected to the internet, there is a general lack of standards compliance and consistency of implementation. Moreover, data collation and generation from IoT enabled medical/mobile devices need specialized
https://azure.microsoft.com/blog/three-things-to-know-about-azure-machine-learning-notebook-vm/Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organization’s security and compliance policies. Data scientists working on machine learning projects need a flexible environment to run experiments, train models, iterate models, READ MORE
This blog post was authored by Jordan Edwards, Senior Program Manager, Microsoft Azure.
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.
Azure Machine Learning service’s MLOps capabilities provide customers with asset management and orchestration services, enabling effective ML lifecycle management. With this announcement, Azure is reaffirming its commitment to help customers safely bring their machine learning models to production and solve their business’s key problems faster and more accurately than ever before.
Here is a quick look at some of the new features:
Azure Machine Learning Command Line Interface (CLI)
Azure Machine Learning’s management plane has historically been via the Python SDK. With the new Azure Machine Learning CLI, you can easily perform a variety of automated tasks against the ML workspace including:
Compute target management Experiment submission Model registration and deployment Management capabilities
Azure Machine Learning service introduced new capabilities to help manage the code, data, and environments used in your ML lifecycle.
Recommendation systems are used in a variety of industries, from retail to news and media. If you’ve ever used a streaming service or ecommerce site that has surfaced recommendations for you based on what you’ve previously watched or purchased, you’ve interacted with a recommendation system. With the availability of large amounts of data, many businesses are turning to recommendation systems as a critical revenue driver. However, finding the right recommender algorithms can be very time consuming for data scientists. This is why Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.
What is a recommendation system?
There are two main types of recommendation systems: collaborative filtering and content-based filtering. Collaborative filtering (commonly used in e-commerce scenarios), identifies interactions between users and the items they rate in order to recommend new items they have not seen before. Content-based filtering (commonly used by streaming services) identifies features about users’ profiles or item descriptions to make recommendations for new content. These approaches can also be combined for a hybrid approach.
Recommender systems keep customers on a businesses’ site longer, they interact with more products/content, and it