Make your case to attend

DASH, Datadog’s annual conference, is happening on June 09–10 in New York City, and I’d like to attend. This event is packed with valuable hands-on training and best practices for improving model reliability, performance, security, and iteration speed across the AI stack.

How Attending DASH Can Benefit Our Team

End-to-end AI observability: I’ll learn how engineering teams monitor model behavior, drift, latency, GPU utilization, and cost across inference pipelines, embeddings, agentic workflows, and orchestration layers using Datadog’s AI Observability suite.

Accelerating iteration with experimentation workflows: Sessions will cover techniques for evaluating prompts, comparing model variants, and improving development velocity with Datadog’s LLM Experiments and telemetry-driven feedback loops.

Strengthening AI reliability and security: DASH includes sessions on detecting prompt-level anomalies, preventing jailbreaks and data exfiltration, and securing agentic systems using tools like Datadog’s AI Guard and Sensitive Data Scanner.

Agentic AI for faster triage and debugging: Datadog will unveil their latest agentic AI capabilities that correlate code, telemetry, and runtime behavior,—so our teams can diagnose issues faster and streamline root-cause analysis in complex AI workflows.

My Focus Areas for the Event

I will prioritize workshops and discussions relevant to our roadmap, including:

• [Project, workload or initiative]
• [Project, workload or initiative]

DASH will also give me direct access to product leaders, researchers, and engineers working on GPU monitoring, model evaluation, and agentic automation.This is the perfect opportunity to validate our approaches and explore new ways to improve observability and reliability across our AI stack.

Attending DASH will help improve the performance, security, and resilience of our AI applications, while accelerating our ability to iterate and ship improvements safely.