Bringing Observability to Complex AI Platforms and Models

About the webinar

With 99% of Fortune 500 companies and 31% of SMBs already using AI — and many more planning to adopt — failures could trigger cascading effects. It is critical for organizations to closely monitor the health of their AI platforms. However, monitoring AI platforms and models is far more complex than traditional monitoring. From workloads and the ML lifecycle to infrastructure and hardware, every component is built differently, posing unique challenges such as multi-stage pipelines, dynamic workloads, model drift, and hardware temperature management.

Observability tools like Nvidia DCGM, OpenTelemetry, and Prometheus can help you implement an observability stack. However, identifying the influential metrics, logs, and traces is key to uncovering the factors that determine AI platforms’ health. By shedding light on GPU utilization, performance, model drift, and LLM accuracy, we can optimize GPU resource sharing and extend the lifespan of AI platforms.

Join the webinar to learn how to overcome all the observability challenges and effectively monitor AI platforms and models deployed on Kubernetes. We will also share our AI Stack deployed on Kubernetes, showcasing the use of open source observability tools like Prometheus, Grafana, and Nvidia DCGM to comprehensively monitor real-time metrics from our GPU clusters, inference, and embeddings servers.