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Dynatrace Expands Davis AI Engine

Dynatrace is expanding its Davis® AI engine to create hypermodal artificial intelligence (AI), converging fact-based, predictive- and causal-AI insights with new generative-AI capabilities.

The expanded Davis AI will boost productivity across business, development, security, and operations teams by delivering generative-AI recommendations fueled by precise context from predictive- and causal-AI techniques that reflect the unique attributes of each organization’s hybrid and multicloud ecosystem. It will also simplify and accelerate tasks, such as creating automations and dashboards, to enable people to focus on higher-value activities for faster, better, and more secure innovation.

Dynatrace Davis AI provides these capabilities.

- Davis predictive-AI models and dynamic machine learning anticipate future behavior based on past data and observed patterns. This capability allows customers to anticipate and remediate future needs and issues related to the performance and security of their software.

- Davis causal AI analyzes real-time and context-rich observability, security, and business data within the Grail™ data lakehouse and causal dependencies from Smartscape® topology to provide the precise answers and intelligent automation that are necessary for issue prevention, deterministic root-cause analysis, and automated risk remediation.

- Davis CoPilot™ generative AI works with Dynatrace® predictive and causal AI to automatically provide recommendations, create suggested workflows and dashboards, or let people use natural language to explore, solve, and complete tasks.

“Generative AI is a transformative technology with seemingly limitless possibilities for delivering productivity gains,” said Bernd Greifeneder, CTO at Dynatrace. “As organizations look to tap into this potential, the key to success is hypermodal AI that combines generative AI with powerful predictive- and causal-AI techniques. This is because only predictive AI can see into the future reliably, only causal AI can deterministically know the root cause of an issue, and only generative AI can tailor recommendations and solutions to specific problems using advanced probabilistic algorithms. With the release of the expanded Davis AI, we address this need and redefine how observability and security solutions work. We expect Davis AI will enable our customers to achieve substantial productivity gains year-over-year as they drive transformation initiatives related to observability and security.”

Davis predictive and causal AI are available now for all customers, while the expansion to include Davis CoPilot will be available later in 2023 and accessible to all customers as a core technology within the Dynatrace platform.

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Dynatrace Expands Davis AI Engine

Dynatrace is expanding its Davis® AI engine to create hypermodal artificial intelligence (AI), converging fact-based, predictive- and causal-AI insights with new generative-AI capabilities.

The expanded Davis AI will boost productivity across business, development, security, and operations teams by delivering generative-AI recommendations fueled by precise context from predictive- and causal-AI techniques that reflect the unique attributes of each organization’s hybrid and multicloud ecosystem. It will also simplify and accelerate tasks, such as creating automations and dashboards, to enable people to focus on higher-value activities for faster, better, and more secure innovation.

Dynatrace Davis AI provides these capabilities.

- Davis predictive-AI models and dynamic machine learning anticipate future behavior based on past data and observed patterns. This capability allows customers to anticipate and remediate future needs and issues related to the performance and security of their software.

- Davis causal AI analyzes real-time and context-rich observability, security, and business data within the Grail™ data lakehouse and causal dependencies from Smartscape® topology to provide the precise answers and intelligent automation that are necessary for issue prevention, deterministic root-cause analysis, and automated risk remediation.

- Davis CoPilot™ generative AI works with Dynatrace® predictive and causal AI to automatically provide recommendations, create suggested workflows and dashboards, or let people use natural language to explore, solve, and complete tasks.

“Generative AI is a transformative technology with seemingly limitless possibilities for delivering productivity gains,” said Bernd Greifeneder, CTO at Dynatrace. “As organizations look to tap into this potential, the key to success is hypermodal AI that combines generative AI with powerful predictive- and causal-AI techniques. This is because only predictive AI can see into the future reliably, only causal AI can deterministically know the root cause of an issue, and only generative AI can tailor recommendations and solutions to specific problems using advanced probabilistic algorithms. With the release of the expanded Davis AI, we address this need and redefine how observability and security solutions work. We expect Davis AI will enable our customers to achieve substantial productivity gains year-over-year as they drive transformation initiatives related to observability and security.”

Davis predictive and causal AI are available now for all customers, while the expansion to include Davis CoPilot will be available later in 2023 and accessible to all customers as a core technology within the Dynatrace platform.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...