Session Name: Cutting MTTR with Generative AI: Our Journey to Precise Recommendations
Companies collect increasing amounts of operational data from their systems for monitoring and observability purposes, but then find themselves with fewer insights about their systems, and with an ever-increasing mean time to resolve IT issues (MTTR). In a recent survey, around 64% reported that their MTTR during production incidents was over an hour. Can ChatGPT and Generative AI rescue us? In this talk, Horovits will share his company’s journey to harness the power of AI and machine learning to provide accurate and effective IT issue resolution. He will share how they developed precise recommendations based on contextual data, including users' search behavior. You will learn how Generative AI offers additional investigative paths to the problem, leading to more accurate contextualized recommendations, which end up cutting Mean Time to Remediation from hours to minutes. If you suffer from too much data and too few insights, this talk is for you
Speaker Bio:
Horovits lives at the intersection of technology, product and innovation. With over 20 years in the tech industry as a developer, an architect and a product manager, he brings a wealth of knowledge in cloud and cloud-native solutions, DevOps practices and more. Horovits is an international speaker and thought leader, as well as an Ambassador of the Cloud Native Computing Foundation (CNCF). Horovits is an organizer of the Israeli CNCF chapter, Kubernetes Community Days and DevOpsDays, a podcaster at OpenObservability Talks, and a blogger, among others. As the principal developer advocate at Logz.io, Horovits evangelizes on Observability using popular open source projects such as Prometheus, OpenSearch, Jaeger and OpenTelemetry.
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