Artificial intelligence has reshaped how companies serve products, interact with customers, market themselves and measure performance. The technology is also revamping data visibility, with AI-powered observability deployed to glean deep-lying insights from data resources spread across disparate cloud deployments.

Visibility is key to business resilience, and resilience is key to business success. Dynatrace LLC and Microsoft Corp. have combined cutting-edge AI tools and robust cloud infrastructure to deliver AI-powered observability, driving the expedient discovery and remediation of multimodal data issues.

“Our SaaS offering is running on Azure, providing our customers a native experience to have observability available from Dynatrace as they’re using their cloud environments,” said Alois Reitbauer (pictured, left), chief technology strategist of Dynatrace LLC. “What we provide is observability and security for their more traditional workloads in addition to their AI workloads with AI observability. Obviously, it’s also key to have a partner like Microsoft and the cloud infrastructure that allows to do this in a very automated way.”

Reitbauer and Eve Psalti (right), senior director of AI at Microsoft Corp., spoke with theCUBE Research’s Rob Strechay, during an exclusive CUBE Conversation, focused on “Next-Gen Observability to Ensure Business Resiliency.” They discussed AI-powered observability as a competitive advantage for enterprises navigating modern cloud challenges. (* Disclosure below.)

AI-powered observability expands the cloud transformation horizon

Observability, the practice of monitoring and analyzing systems to detect anomalies and optimize performance, is vital for AI-driven applications. For enterprises leveraging AI and cloud technologies, observability offers critical insights into infrastructure and application behavior, according to Psalti.

“We’re saying that you don’t know what you can’t see,” she said. “Basically, observability becomes a critical factor in helping customers optimize their infrastructure and applications. It provides visibility into the behavior and the performance of the AI models. It also helps identify shifts in data patterns — what we call data detection. It helps identify root cause analysis for failure, and it’s a resource to further optimize your infrastructure by monitoring usage and identifying any patterns that you need to.”

When paired with cloud-native capabilities, observability becomes a tool for troubleshooting as well as innovation, enabling businesses to deliver better customer experiences and achieve faster time-to-market.

For Microsoft, the alliance with Dynatrace transforms Azure’s powerful AI and cloud capabilities into actionable business value. By embedding Dynatrace’s observability tools into Azure, enterprises gain a comprehensive view of their systems. This synergy allows companies to harness advanced insights, ensuring applications are secure, reliable and scalable, Psalti added.

“We are building the platform, whether it’s at the cloud base or with the AI capabilities that we bring in,” she said. “We depend on partners like Dynatrace to activate the business value. We know that getting advanced insights and security insights is top-of-mind for customers. Having a deep and robust observability platform on top of your cloud and AI infrastructure is absolutely key.”

Navigating the complexities of AI and cloud-native environments

AI and cloud technologies are reshaping enterprise operations. Modern digital-first processes, such as automated customer interactions, require increasingly complex backend systems. These systems consist of myriad interconnected services and microservices that must operate seamlessly.

Trends such as gen AI add another layer of complexity. Applications leveraging AI models, from language generation to predictive analytics, require real-time monitoring to ensure performance and prevent downtime, according to Reitbauer.

“We can’t afford downtime anymore, and we are constantly introducing new technologies like generative AI, for example,” he said. “These environments have become more complex and harder to manage. Applying very traditional approaches to manage those applications would not work. That’s how observability and also AI observability comes into play, really supporting the people who have to keep these applications that allow these modern experiences to be available for customers.”

Dynatrace’s proprietary hypermodal AI model, known as Davis, exemplifies the fusion of advanced AI techniques to tackle enterprise challenges. Davis integrates predictive AI, causal AI and generative AI to deliver holistic system management, according to Reitbauer.

“The idea is to combine multiple forms of AI to provide the best outcome,” he said. “It starts on the predictive side, understanding how a system should behave. And when it’s not behaving as it’s expected to, this is usually referred to as anomaly detection.”

Predictive AI anticipates system changes, while causal AI determines the relationships between events. Gen AI, powered by robust data inputs, suggests specific actions, such as modifying deployment scripts or resolving user-facing issues.

Here’s theCUBE’s complete video interview with Reitbauer and Psalti:

(* Disclosure: Dynatrace LLC and Microsoft Corp. sponsored this segment of theCUBE. Neither Dynatrace and Microsoft nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE