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Build powerful and responsible AI solutions with Azure

As organizations assess safely reopening and continue navigating unexpected shifts in the world, getting insights to respond in an agile and conscientious manner is vital.

As organizations assess safely reopening and continue navigating unexpected shifts in the world, getting insights to respond in an agile and conscientious manner is vital. Developers and data scientists of all skill levels are inventing with Microsoft Azure AI's powerful and responsible tools to meet these challenges.

Operating safely

To help organizations operate safely in today’s environment, we are introducing a new spatial analysis capability in the Computer Vision Azure Cognitive Service. Its advanced AI models aggregate insights from multiple cameras to count the number of people in the room, measure the distance between individuals, and monitor wait and dwell times. Organizations can now apply this technology to use their space in a safe, optimal way. For instance, RXR, one of New York City’s largest real estate companies, has embedded spatial analysis in their RxWell app to ensure occupants' safety and wellness.

“When it came to developing RxWell, there was simply no other company that had the capability and the infrastructure to meet our comprehensive data, analytics, and security needs than Microsoft. With our partnership, the RxWell program provides our customers the tools they need to safely navigate the ‘new abnormal’ of COVID-19 and beyond.” – Scott Rechler, Chairman and CEO, RXR Realty

Read more about the RXR customer story here.

Achieving agility and resiliency

To get timely insights into their business, organizations need to monitor metrics proactively and quickly diagnose issues as they arise. Metrics Advisor, a new Azure Cognitive Service, helps customers to do this through a powerful combination of real-time monitoring, auto-tuning AI models, alerting, and root cause analysis. It allows organizations to fix issues before they become significant problems. No machine learning expertise is required. Customers such as NOS telecommunications have been able to increase agility and improve customer service using Metrics Advisor. 

“Metrics Advisor helps capture potential network device failures in time so that we can react instantly. It reduces incoming customer call bottlenecks and improves customer satisfaction. “ – João Ferreira, Director of Product Development, NOS telecommunications company (Portugal)

To help customers build custom machine learning models without data science expertise, Azure Machine Learning’s no-code automated machine learning and drag and drop designer are now generally available. These capabilities empower citizen data scientists and developers to build machine learning solutions.

“By using Azure Machine Learning designer, we were able to quickly release a valuable tool built on machine learning insights, that predicted occupancy in trains, promoting social distancing in the fight against Covid-19. ” – Steffen Pedersen, Head of AI and advanced analytics, DSB 

We are also making machine learning more accessible by providing additional value at a lower cost. Azure Machine Learning customers will now get the all the Enterprise edition capabilities in the Basic edition at no extra charge, helping them adopt and scale machine learning more cost effectively. Learn more about updates to Azure Machine Learning.

Applying AI responsibly

Safe and responsible use of AI is essential as organizations, and the world, depend on technology more than ever before. Responsible AI practices and guidelines for safe use are infused into Azure AI’s services, such as spatial analysis, to ensure personal privacy, transparency, and trust. We’ve also seen the rapid adoption of Azure Machine Learning’s responsible ML capabilities and toolkits.

A recent example is Philips, a leading health technology company, who’s using the Azure and Fairlearn toolkit to build unbiased machine learning models. Healthcare models can be biased depending on how different hospitals document symptoms and tasks. Using the Fairlearn toolkit, Philips was able to assess key fairness metrics to uncover model inaccuracies for different patient groups. By improving their models’ overall fairness and mitigating biases, they were able to deliver valuable insights to their hospitals on patient wellbeing and care.

With these innovations, all developers and data scientists can harness the power of Azure AI responsibly to help their organizations move forward. For more on the latest, check out these resources:

Azure. Invent with purpose.