Today’s application development and release cycles are measured in days, and not in months. Configuration options of the tech stack continue to increase in scope and complexity, with dependencies that have become unpredictable. Without automation, Performance engineers and developers can no longer ensure that applications perform as planned, and costs are minimized.
Autonomous Performance Optimization, the novel approach that Akamas proposes, automates the performance optimization process with minimal intervention and oversight from human experts. It uses AI and machine learning techniques to automatically and continuously optimize the technology stack, delivering unprecedented application performance, deployment agility, and cost savings.
In this episode of the Performance Clinic series, Stefano Doni (Akamas) joins Andreas Grabner (Dynatrace) to explain how Autonomous Performance Optimization works. After that, he will walk you through the practical steps needed to automate optimizations of your enterprise, cloud, or database systems leveraging observability data from Dynatrace.
Stefano is obsessed with performance optimization and drives Akamas vision for Autonomous Performance Optimization powered by AI. Stefano has more than 15 years of experience in Performance Engineering and led Moviri Research & Development team before co-founding Akamas. He is a regular speaker at international conferences and in 2015 he won the Computer Measurement Group Best Paper Award.
Andreas is a DevOps Activist at Dynatrace and the host of the PurePerformance podcast. He has more than 20+ years of experience as a software developer, tester, and architect and is an advocate for high-performing cloud-scale applications. He is a regular contributor to the DevOps community, a frequent speaker at technology conferences, and regularly publishes articles.
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