Lior Gavish

Lior Gavish, CTO and co-founder of Monte Carlo Data, dives into the shift from data observability to agent observability. They explore how AI is transforming data teams into data-and-AI teams and discuss the broad adoption of agents across industries. Lior emphasizes the importance of capturing granular telemetry to understand complex agent decisions and the challenges of measuring output quality with traditional methods. He introduces automated troubleshooting agents and highlights the critical role of observability for optimizing AI performance.

Linda Gray, founder of Mastertech.ai, shares her journey from tech leadership at Microsoft to transforming auto repair with AI. She discusses how innovative technologies simplify vital repair processes and boost technician efficiency. Lior Gavish, CTO of Monte Carlo, highlights the critical nature of data observability, tackling the challenges of data downtime. Together, they explore the interplay between AI advancements and reliable data systems, revealing how these developments can revolutionize both the auto industry and data management.

Lior Gavish, co-founder of Monte Carlo, discusses how data teams are evolving to support AI features and incorporating AI into their work. Topics include impact of generative AI, integrating AI models into data workflows, challenges in building with generative AI, and optimizing AI solutions for data teams.

Lior Gavish, CTO and Co-founder of Monte Carlo Data, shares his expertise in data observability and reliability. He discusses how organizations are moving from experimentation to production-scale AI deployments. Lior highlights the importance of robust observability tools to prevent human errors and enhance data reliability. The conversation also delves into AI's impact on workflows and the job market, emphasizing the need for engineers to adapt their skills for future opportunities in the evolving tech landscape.

Lior Gavish, CTO, co-founder @ Monte Carlo, talks about data observability and its importance in ensuring reliability and trust in data products. They discuss success stories of selling the idea of data observability, the importance of data quality and governance in versioned data, and how business users can benefit from the data observability platform, Monte Carlo. They also discuss the future of data and mention the challenges of data discovery and the evolution of data management practices.