Observability without AIOps is just Noise

Presented by

Richard Whitehead, CTO, Moogsoft; Kai Rostcheck, Director of Product Marketing, Moogsoft

About this talk

Over the past several years you have likely adopted (or planned to adopt) an observability strategy. As you dig into the Metrics, Logs, and Traces (and maybe Events too), you may have realized that increasing amounts of data don't necessarily make your problems go away. Tune in to hear from Moogsoft CTO, Richard Whitehead and Director of Product Marketing, Kai Rostcheck for a conversation on how to extend Observability into AIOps for a single-pane-of-glass, and the benefits of less noise, fewer incidents, proactive detection, more meaningful insights, and improved automation of your incident management process. The session will cover: - The Evolution of Observability - Common Challenges with Observability - AIOps: What Is It and Where Does It Fit In? - Key Use Cases of AIOps with Observability - Q&A Presenter Bios: Richard Whitehead, CTO Richard Whitehead serves as CTO at Moogsoft. With more than two decades of industry experience Richard previously served as VP of Strategic Technologies at Micromuse (MUSE/IBM) where he was responsible for identifying strategic markets, partnerships and product research, and was instrumental in bringing Netcool (IBM Tivoli Netcool) to market. Kai Rostcheck, Director of Product Marketing Kai enjoys solving puzzles and connecting ideas. He’s spent his career enabling technical solutions for large enterprise environments, and is weirdly fascinated with the elegance of AIOps - turning disparate data into something meaningful, to help people do their jobs better. Kai values both the technical and human sides of User Experience and enjoys personal growth through global travel.
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Moogsoft develops AIOps technology that helps enterprise IT Ops and DevOps teams become faster, smarter and more effective. Moogsoft AIOps’ real-time machine learning algorithms help teams remediate issues that impact their customers’ experience by: • Reducing operational noise (alert fatigue) across your production stack • Proactively detecting Incidents and correlating Events across your monitoring ecosystem • Streamlining collaboration and workflow across teams and toolsets • Codifying knowledge to make operators smarter when encountering future Incidents