This week Dynatrace achieved Amazon Web Services (AWS) Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) category. The designation reflects AWS’ recognition that Dynatrace has demonstrated deep experience and proven customer success building AI-powered solutions on AWS.
This is exciting because we are seeing AI and ML-driven applications maturing rapidly as a way of mastering performance in hybrid, hyper-scale cloud environments. And this is exactly where Dynatrace really shines. With this competency recognition, organizations can easily and confidently engage with experienced partners like Dynatrace on AWS to help them transform to a new generation of automated, enterprise cloud.
Dynatrace – Making the value of AI real
Consider this all-too-familiar challenge: An anomaly in a large microservice application triggers a storm of alerts as services around the globe are impacted. As your application contains literally millions of dependencies, how do you find the original error? Conventional (not built for cloud) monitoring tools are not much help. They collect metrics and raise alerts, but they provide few answers as to what went wrong in the first place.
In contrast, envision an AI-driven intelligent system that accurately provides the answers—in this case, the technical root cause of the anomaly, all contextual and associated information, and how to fix it. This accurate and precise intelligence is now the type of data that can be trusted to trigger auto-remediation processes proactively. This keeps your business performing optimally before most users even notice a glitch.
This is a game-changer in operations for sure, and better yet again when you can apply it along the entire digital value chain, from software development through service delivery to customer interactions. We are moving into the 2020s and smart integration and automation are driving the next innovation cycle in digital transformation and enterprise software.
Other vendors have AI? A brief examination of two different ball games
There are two different categories of AI. There is traditional AI, which is known for things like cutting through noise by reducing alerts. This, however, provides little correlation and coordinating for identifying root cause. It’s a slow jog to the goal post, and it can be inaccurate and limited in its practical applications.
This section will cover the second category with an overview of a different deterministic AI that yields precise, actionable results in real-time. This is immensely powerful, and while it can cut through noise and give you precise answers and more – it can also help you move innovatively forward by enabling automated IT operations.
To start, let’s look at the illustration below to see how these two operate in comparison:
Also to note on traditional machine learning: by its very nature of operation it is slow and less agile. It needs to collect a substantial amount of data at the beginning to build a training dataset that an algorithm can begin to learn from. Users then have the option of reinforcing learning through rating and similar means. Weeks, if not months, can pass until the system is honed to completely trust it with production monitoring of business-critical processes. And then add in that modern enterprise cloud environments are dynamic and constantly changing.
Shown above is traditional AIOps building and rebuilding context from aggregated data vs. Dynatrace built-in deterministic AI operating on its own raw data with topology information
Taking a Walk with Root Cause Analysis using Deterministic AI
Davis—the Dynatrace AI engine—uses the application topology and service flow maps together with high-fidelity metrics to perform a fault tree analysis. Consider the following example visualized in the chart below:
- A web app exhibits an anomaly, like a reduced response time (see top left in the graphic).
- Davis first “takes a look” at the vertical stack below and finds that everything performs as expected—no problems there.
- From here, Davis follows all the transactions and detects a dependency on Service 1 that also shows an anomaly. In addition, all further dependencies (Services 2 and 3) exhibit anomalies as well.
- The automatic root-cause detection includes all the relevant vertical stacks as shown in the example and ranks the contributors to determine the one with the most negative impact.
- In this case, the root cause is a CPU saturation in one of the Linux hosts.
As you can see, a fault tree shows all the vertical and horizontal topological dependencies for a given alert. Today 86% of organizations are using cloud-native technologies, including hybrid, multi-cloud architectures, Kubernetes, microservices, containers – all dynamically changing. To get the most out of all of this environment at scale, and to manage constant change and reduce repetitive manual work, you need the engine power of deterministic AI and the automation it can help you fully leverage.
An Example of Accelerating Modernization and Migration to the Cloud
If you are re-hosting, re-architecting or re-platforming, Dynatrace with AWS provides you with automatic and intelligent actionable insights across each phase of your journey and every step of the way.
This highly valuable because the planning involved in moving these workloads is one of the main factors of why customers move more slowly than they’d like. Across these phases, there are times that there are no clear answers for decisions. Dynatrace with AWS provide these answers clearly, accelerating the quality and timing of success.
There are many other areas where AI plays a powerful role. You can enable autonomous operations, boost innovation, and offer new modes of customer engagement by using AI and automating processes:
Whether it’s the speed and quality of innovation for development, the automated efficiency in operations, or optimization and consistency of user experiences to business outcomes, Dynatrace, AWS, and our recent AWS Machine Learning Competency status, will continue to see us strongly partnering to help organizations dramatically reduces risk and accelerates results.
In summary, a joint AWS and Dynatrace customer, Vitality, health and life insurance provider says it most aptly “We have successfully built our cloud-native applications on AWS, and Dynatrace’s AI and automation ensure they are fast, efficient, and predictable,” said David Priestley, Chief Digital Officer at Vitality. “Dynatrace’s deep integrations with AWS, paired with its AI expertise, enables us to find anomalies in our applications and user journeys before they impact business outcomes. The platform’s automation has enabled us to improve customer experience through faster responses to customer requests and freeing up time for our teams to innovate.”
Dynatrace is a part of the AWS partner network and is an Advanced Technology Partner with Competencies in Containers, DevOps, Migration, and Applied Artificial Intellgience; we are designated as Service Ready for AWS PrivateLink, AWS RDS Ready, Amazon Linus 2 and AWS Outposts; are a Public Sector Partner and SaaS Partner, and a Marketplace Seller.
This syndicated content is provided by Dynatrace and was originally posted at https://www.dynatrace.com/news/blog/dynatrace-achieves-aws-machine-learning-competency-as-it-continues-to-enable-a-new-generation-of-automated-enterprise-cloud/