Organizations today are striving to build agility and resilience to the fast-changing environment we live in. AI and machine learning innovation can help tackle these emerging challenges and enable cost efficiencies. However, organizations still encounter barriers to adopting and deploying machine learning at scale. Recently at Microsoft Ignite, Azure Machine Learning made a number of announcements that help organizations harness machine learning more easily, securely, and at scale. This includes capabilities like designer and automated machine learning UI, now generally available, that simplify machine learning for beginners and professionals alike. Advanced role-based access control (RBAC) and private IP link, in preview, make it possible to build machine learning solutions more securely. In addition, we are merging the Azure Machine Learning Enterprise and Basic Editions to deliver greater value at no extra cost.
“With Azure Machine Learning, we’re increasing speed-to-value while reducing cost-to-value.” – Sarah Dods, Head of Advanced Analytics, AGL. Read the story.
Machine learning simplified
Azure Machine Learning designer provides a drag-and-drop canvas to build no-code models with ease. Built-in modules help preprocess data and build and train models using machine learning and deep learning algorithms, including computer vision, text analytics, recommendation, anomaly detection, and
Organizations around the world are gearing up for a future powered by artificial intelligence (AI). From supply chain systems to genomics, and from predictive maintenance to autonomous systems, every aspect of the transformation is making use of AI. This raises a very important question: How are we making sure that the AI systems and models show the right ethical behavior and deliver results that can be explained and backed with data?
This week at Spark + AI Summit, we talked about Microsoft’s commitment to the advancement of AI and machine learning driven by principles that put people first.
Understand, protect, and control your machine learning solution
Over the past several years, machine learning has moved out of research labs and into the mainstream and has grown from a niche discipline for data scientists with PhDs to one where all developers are empowered to participate. With power comes responsibility. As the audience for machine learning expands, practitioners are increasingly asked to build AI systems that are easy to explain and that comply with privacy regulations.
The demand for artificial intelligence (AI) and data science roles continues to rise. According to LinkedIn’s Emerging Jobs Report for 2020, AI specialist roles are most sought after with a 74 percent annual growth rate in hiring over the last four years. Additionally, the current global health pandemic has powered a shift towards remote working as well as an increased interest in professional training resources. To address this demand, we’re announcing our collaboration with Udacity to launch new machine learning courses for both beginners and advanced users, as well as a scholarship program.
Through these new offerings, Microsoft aims to help expand the talent pool of data scientists and improve access to education and resources to anyone interested. I recently sat down for a chat with Udacity CEO, Gabe Dalporto, to talk about this collaboration.
Udacity is a digital education platform with over 250,000 currently active students. Their students have expressed continued interest in introductory machine learning (ML) content that doesn’t require advanced programming knowledge. In response, Microsoft Azure and Udacity have created a unique free course based on Azure Machine Learning. This Introduction to machine learning on Azure course will help students learn the basics of ML through
Machine learning (ML) is gaining momentum across a number of industries and scenarios as enterprises look to drive innovation, increase efficiency, and reduce costs. Microsoft Azure Machine Learning empowers developers and data scientists with enterprise-grade capabilities to accelerate the ML lifecycle. At Microsoft Build 2020, we announced several advances to Azure Machine Learning across the following areas: ML for all skills, Enterprise grade MLOps, and responsible ML.
ML for all skills
New enhancements provide ML access for all skills.
Enhanced notebook in preview
Data scientists and developers can now access an enhanced notebook editor directly inside Azure Machine Learning studio. New capabilities to create, edit, and collaborate make remote work and sharing easier for data science teams and the notebook is fully compatible with Jupyter.
Boost development productivity with features like IntelliSense, inline error highlighting, and code suggestions from VSCode, which deliver the best-in-class coding experience in Jupyter notebooks. Access real-time co-editing (coming soon) for seamless remote collaboration or pair debugging. Inline controls to start, stop, and create a new compute using GPU or CPU Compute Instance inside notebooks. Add new kernels to the notebook editor and quickly switch between different kernels like Python and R.
Real-time notebook co-editing
It’s inspiring to see how customers continue to reimagine how they work with the help of AI, which is more important today than ever. Our customers are finding innovative ways to deliver crisis management solutions, drive cost-savings, redefine customer engagement, and accelerate decision-making.
Here are some notable examples we’ve recently seen:
Scaling crisis management
On the frontlines, first responders rely on Azure AI to scale their triage process to address the overwhelming number of people needing care and to ease volume in the system. For example, healthcare providers have created more than 1,400 bots using our Healthcare Bot service, helping more than 27 million people access critical healthcare information. The U.S. Centers for Disease Control and Prevention released a COVID-19 assessment bot that is powered by Azure Bot Service. Motorola Solutions uses Azure Bot Service, as well as speech and language services, in its own voice assistant for public safety, ViQi, to help 911 dispatchers and first responders focus on what matters most.
Azure AI is also helping customers optimize their operations to reduce costs. KPMG built a risk and fraud analytics solution using our speech and language services to streamline call center transcription and translation—cutting time,
As AI reaches critical momentum across industries and applications, it becomes essential to ensure the safe and responsible use of AI. AI deployments are increasingly impacted by the lack of customer trust in the transparency, accountability, and fairness of these solutions. Microsoft is committed to the advancement of AI and machine learning (ML), driven by principles that put people first, and tools to enable this in practice.
In collaboration with the Aether Committee and its working groups, we are bringing the latest research in responsible AI to Azure. Let’s look at how the new responsible ML capabilities in Azure Machine Learning and our open-source toolkits empower data scientists and developers to understand ML models, protect people and their data, and control the end-to-end ML process.
As ML becomes deeply integrated into our daily business processes, transparency is critical. Azure Machine Learning helps you to not only understand model behavior but also assess and mitigate unfairness.
Interpret and explain model behavior
Model interpretability capabilities in Azure Machine Learning, powered by the InterpretML toolkit, enable developers and data scientists to understand model behavior and provide model explanations to business stakeholders and customers.
Use model interpretability to:
Build accurate ML models.
Digital transformation in manufacturing has the potential to increase annual global economic value by $4.5 trillion according to the IDC MarketScape.iWith so much upside, manufacturers are looking at how technologies like IoT, machine learning, and artificial intelligence (AI) can be used to optimize supply chains, improve factory performance, accelerate product innovation, and enhance service offerings.
Digital transformation starts by collecting data from machines on the plant floor, assets in the supply chain, or products being used by customers. This data can be combined with other business data and then modeled and analyzed to gain actionable insights.
Let’s take a look at three manufacturers—Festo, Kao, and AkzoNobel—and see how each one is using technologies like IoT, machine learning, and AI to accelerate their digital transformation.
Providing predictive maintenance as a service
Based in Germany, Festo sells electric and pneumatic drive solutions to 300,000 customers in 176 countries. The company’s goal is to increase uptime for customers by providing predictive maintenance offerings as software as a service (SaaS) offerings. Festo’s strategy is to connect machines to the cloud with Azure IoT and then enable customers to visualize data along the entire value chain.
One of the first SaaS offerings is Festo
https://azure.microsoft.com/blog/extending-the-power-of-azure-ai-to-microsoft-365-users/Today, Yusuf Mehdi, Corporate Vice President of Modern Life and Devices, announced the availability of new Microsoft 365 Personal and Family subscriptions. In his blog, he shared a few examples of how Microsoft 365 is innovating to deliver experiences powered READ MORE
If you use Office 365, you have likely seen the Microsoft PowerPoint Designer appear to offer helpful ideas when you insert a picture into a PowerPoint slide. You may also have found it under the Home tab in the ribbon. In either case, Designer provides users with redesigned slides to maximize their engagement and visual appeal. These designs include different ways to represent your text as diagrams, layouts to make your images pop, and now it can even surface relevant icons and images to bring your slides to the next level. Ultimately, it saves users time while enhancing their slides to create stunning, memorable, and effective presentations.
Designer uses artificial intelligence (AI) capabilities in Office 365 to enable users to be more productive and unlock greater value from PowerPoint. It applies AI technologies and machine learning based techniques to suggest high-quality professional slide designs. Content on slides such as images, text, and tables are analyzed by Designer and formatted based on professionally designed templates for enhanced effectiveness and visual appeal.
The data science team, working to grow and improve Designer, is comprised of five data scientists with diverse backgrounds in applied machine learning and software engineering. They strive to continue
Azure Container Registry announces preview support for Azure Private Link, a means to limit network traffic of resources within the Azure network.
With Private Link, the registry endpoints are assigned private IP addresses, routing traffic within a customer-defined virtual network. Private network support has been one of the top customer asks, allowing customers to benefit from the Azure management of their registry while benefiting from tightly controlled network ingress and egress.
Private Links are available across a wide range of Azure resources with more coming soon, allowing a wide range of container workloads with the security of a private virtual network.
Private Endpoints and Public Endpoints
Private Link provides private endpoints to be available through private IPs. In the above case, the contoso.azurecr.io registry has a private IP of 10.0.0.6 which is only available to resources in contoso-aks-eastus-vnet. This allows the resources in this VNet to securely communicate. The other resources may be restricted to resources only within the VNet.
At the same time, the public endpoint for the contoso.azurecr.io registry may still be public for the development team. In a coming release, Azure Container Registry (ACR) Private Link will support disabling the public endpoint, limiting access to