Our team has been delivering outstanding talks at conferences and leading quarterly training sessions for Java developers, SRE engineers, Performance QA engineers, and DevOps engineers. Over the past year, we’ve also been running a series of webinars, where our very own Ram Lakshmanan shared deep insights into Java performance and troubleshooting. We recently celebrated this journey in our Java Performance Webinars Milestone Blog.
Now, we’re excited to take this initiative to the next level. Moving forward, our monthly webinars will not only feature in-house expertise but also bring in renowned external speakers from the global Java community. These sessions are designed to deliver valuable techniques, best practices, and tools to help you optimize Java performance and solve complex troubleshooting challenges.
This blog category will keep you updated with details on upcoming events as well as recaps of past sessions. Stay tuned for expert insights, practical takeaways, and new learning opportunities every month!
Upcoming Webinar
- Date: April 14, 2026
- Time: 8:30 AM PST
- Duration: 1 hour
Speaker Bio
Ram Lakshmanan, Architect of yCrash
He is the architect behind widely used DevOps tools such as GCeasy, fastThread, and HeapHero. He has built large-scale systems powering banking, travel, and commerce platforms used by millions across North America. Ram also advises startups, Fortune 500 enterprises, and government organizations on production troubleshooting, performance engineering, and AI-driven incident analysis.
Mahesh Devda, Architect at yCrash
He is the architect behind HeapHero and specializes in deep heap dump analysis and memory troubleshooting. He has extensive hands-on experience diagnosing complex production issues and optimizing application performance. He is also emerging as a go-to expert in applying AI-driven approaches to incident analysis, helping teams simplify debugging and accelerate root cause identification.
Title:
Troubleshooting .NET Production problems using AI
Description:
In 2011, software ate every business.
In 2026, AI is eating software.
AI is now everywhere, yet many teams still troubleshoot .NET production issues the same way they did over a decade ago.
During critical outages, traditional troubleshooting approaches are often slow, manual, and heavily dependent on individual expertise. This results in longer downtime and increased business impact.
In this session, you will learn a modern, AI-driven approach to production troubleshooting.
We will walk through the 16 essential diagnostic artifacts required to effectively analyze .NET production problems. You’ll also see how machine learning techniques and conversational AI can accelerate analysis, helping teams move from reactive debugging to intelligent, guided troubleshooting.
The goal is simple: Reduce your Mean Time to Recovery (MTTR) from hours to seconds.
Key Takeaways:
- The 16 critical artifacts required to troubleshoot .NET production issues effectively
- Why traditional troubleshooting approaches slow down incident resolution
- How AI and ML algorithms can automatically analyze diagnostic data
- Using conversational AI to triage and guide debugging workflows
- How to correlate multiple artifacts for faster root cause identification
- Strategies to reduce dependency on manual expertise during incidents
- How to significantly reduce MTTR from hours to seconds
- Building a modern, AI-driven troubleshooting workflow for .NET applications
- Live Q&A Session with our architects Ram Lakshmanan & Mahesh Devda