One of the most important considerations when choosing an AI service is security and regulatory compliance. Can you trust that the AI is being processed with the high standards and safeguards that you come to expect with hardened, durable software systems?
Cognitive Services today includes 14 generally available products. Below is an overview of current certifications in support of greater security and regulatory compliance for your business.
Added industry certifications and compliance
Significant progress has been made in meeting major security standards. In the past six months, Cognitive Services added 31 certifications across services and will continue to add more in 2019. With these certifications, hundreds of healthcare, manufacturing, and financial use cases are now supported.
The following certifications have been added:
ISO 20000-1:2011, ISO 27001:2013, ISO 27017:2015, ISO 27018:2014, and ISO 9001:2015 certification HIPAA BAA HITRUST CSF certification SOC 1 Type 2, SOC 2 Type 2, and SOC 3 attestation PCI DSS Level 1 attestation
For additional details on industry certifications and compliance for Cognitive Services, visit the Overview of Microsoft Azure Compliance page.
Enhanced data storage commitments
Cognitive Services now offers more assurances for where customer data is stored at rest. These assurances have been enabled by graduating
The year 2018 was a banner year for Azure AI as over a million Azure developers, customers, and partners engaged in the conversation on digital transformation. The next generation of AI capabilities are now infused across Microsoft products and services including AI capabilities for Power BI.
Here are the top 10 Azure AI highlights from 2018, across AI Services, tools and frameworks, and infrastructure at a glance:
3. Microsoft is first to enable Cognitive Services in containers.
4. Cognitive Search and basketball
AI tools and frameworks
7. Open Neural Network Exchange (ONNX) runtime is now open source.
10. Project Brainwave, integrated with AML.
With many exciting developments, why are these moments the highlight? Read on, as this blog begins to explain the importance of these moments.
These services span pre-built
Developers can now access the latest improvements to Cognitive Services Speech Service including a new Python API and more. Details below.
Read the updated Speech Services documentation to get started today.
Support for Ubuntu 18.04 is now available in addition to pre-existing support for Ubuntu 16.04.
New features by popular demand Lightweight SDK for greater performance
By reducing the number of required concurrent threads, mutexes, and locks, Speech Services now offers a more lightweight SDK with enhanced error reporting.
Control of server connectivity and connection status
A newly added connection object enables control over when the SDK connects to the Speech Service. You
This blog post was co-authored by Vishwac Sena Kannan, Principal Program Manager, FUSE Labs.
We are thrilled to present the release of Bot Framework SDK version 4.2 and we want to use this opportunity to provide additional updates on Conversational-AI releases from Microsoft.
In the SDK 4.2 release, the team focused on enhancing monitoring, telemetry, and analytics capabilities of the SDK by improving the integration with Azure App Insights. As with any release, we fixed a number of bugs, continued to improve Language Understanding (LUIS) and QnA integration, and enhanced our engineering practices. There were additional updates across the other areas like language, prompt and dialogs, and connectors and adapters. You can review all the changes that went into 4.2 in the detailed changelog. For more information, view the list of all closed issues.
Telemetry updates for SDK 4.2
With the SDK 4.2 release, we started improving the built-in monitoring, telemetry, and analytics capabilities provided by the SDK. Our goal is to provide developers with the ability to understand their overall bot-health, provide detailed reports about the bot’s conversation quality, as well as tools to understand where conversations fall short. To do that, we decided to further enhance the built-in
Applying the latest in deep learning innovation, Speech Service, part of Azure Cognitive Services now offers a neural network-powered text-to-speech capability. Access the preview available today.
Neural Text-to-Speech makes the voices of your apps nearly indistinguishable from the voices of people. Use it to make conversations with chatbots and virtual assistants more natural and engaging, to convert digital texts such as e-books into audiobooks and to upgrade in-car navigation systems with natural voice experiences and more.
This release includes significant enhancements since we first revealed Neural Text-to-Speech at Ignite earlier this year.
Enhanced voice quality
The voices sound more robust and natural across a wider variety of user scenarios, achieved by harnessing the following:
A large supervised training with transfer learning across diverse speakers More features from unsupervised pretraining Added robust neural model design Accelerated runtime performance
Runtime performance of the Neural Text-to-Speech engine is near-instantaneous through extensive code optimization with hardware accelerators, applying parallel inference models and model simplifications considering the balance of sound quality and performance. The real-time factor has been improved from the previous version to less than 0.05X, meaning 1 second of audio can be generated in less than 50 milliseconds. Producing the first byte of
I’m Anna Thomas, an Applied Data Scientist within Microsoft Engineering. My goals are to enable the field and partners to better integrate various AI tools into their applications. Recently, my team reached out to Microsoft News to see how they’re analyzing their data, and how our services may be able to help.
Microsoft News ingests more than 100,000 articles and videos every day from various news providers. With so many different aspects such as classifying news topics, tagging and translating content, I was immediately interested in understanding how they process all of that information.
As it turns out, Microsoft News has been working on some pretty advanced algorithms that analyze their articles and determine how to increase personalization, which ultimately increases consumption, for years. However, when I asked them if there were any gaps, they were quick to answer that they would love more insight on their videos.
Analyzing videos at scale to obtain insights is a nontrivial task. Having insights on videos, especially for a news platform, can help with increasing search quality, user engagement through personalization, and the accessibility of videos through captioning, translating, and more. There are so many different aspects related to this: classifying different news
Pharmaceutical companies need to meet demanding sales goals, manage intricate regulatory compliance, and maintain a competitive hold on the market. However, current sales force automation (SFA) solutions for the life sciences industry are focused primarily on sales reps, which leaves a large capability gap for sales operations departments and inhibits their ability to support the sales process.
Prescriber360 is a Microsoft Gold Partner with a comprehensive Pharma SalesOps solution designed specifically for the life sciences industry that can reduce, and even close, the capability gap.
Problem: Data spread out and unavailable
Pharma and biotech industries rely heavily on both internal and external data sources. However, most sales operations processes are currently performed manually in spreadsheets and without a centralized system of record. This makes it incredibly difficult to access and leverage that data.
The following problems often result:
Sales teams built around multiple therapeutic areas and prescriber specialties present a complex targeted environment requiring powerful systems and processes. Quarterly alignment processes require touching an entire customer universe, and it takes a lot of effort to update an SFA system with new information, maintain a history of changes to support incentive compensation (IC), and preview the impact of alignments on
The healthcare industry has started to embrace mobile apps and cloud technologies, and not just to optimize internal operations. Cincinnati Children’s Hospital Medical Center wants to make visits to hospitals and doctors’ offices easier for patients and families. Cincinnati Children’s serves a diverse population, but people typically have one thing in common—they’re stressed out. Parents are worried about their children, and children can be scared and overwhelmed by all the hustle and bustle around them. So the hospital came up with the idea of a mobile digital concierge who could provide basic information and also answer specific questions about a family’s appointments and the child’s procedures.
The Cincinnati Children’s development team didn’t have expertise building mobile apps, but they did have lots of experience developing in .NET. The team also knew it needed a cloud deployment platform that would support the functionality they had in mind and would also grow with them as they expand and scale the app. Cincinnati Children’s was already using Azure DevOps, so it went with Microsoft Visual Studio Tools for Xamarin for cross-platform app development and chose Azure as its cloud platform. Azure offered a wide variety of services they could use without purchasing third-party
Last week we announced a preview of Docker support for Microsoft Azure Cognitive Services with an initial set of containers ranging from Computer Vision and Face, to Text Analytics. Here we will focus on trying things out, firing up a cognitive service container, and seeing what it can do. For more details on which containers are available and what they offer, read the blog post “Getting started with these Azure Cognitive Service Containers.”
You can run docker in many contexts, and for production environments you will definitely want to look at Azure Kubernetes Service (AKS) or Azure Service Fabric. In subsequent blogs we will dive into doing this in detail, but for now all we want to do is fire up a container on a local dev-box which works great for dev/test scenarios.
You can run Docker desktop on most dev-boxes, just download and follow the instructions. Once installed, make sure that Docker is configured to have at least 4G of RAM (one CPU is sufficient). In Docker for Windows it should look something like this:
Getting the images
The Text Analytics images are available directly from Docker Hub as follows:
Key phrase extraction extracts key talking
https://azure.microsoft.com/blog/getting-started-with-azure-cognitive-services-in-containers/Building solutions with machine learning often requires a data scientist. Azure Cognitive Services enable organizations to take advantage of AI with developers, without requiring a data scientist. We do this by taking the machine learning models and the pipelines and READ MORE