For an external observer, the progress in multiple fields within the AI domain appears to have been exponential in the past few months. The industry is growing at a very fast pace, which has direct implications on the evolution of the underlying infrastructure that's powering the AI workloads. AI workloads efficiently utilize the high parallelism capabilities of GPU units. The spike in the price of NVIDIA stocks and the birth of the whole new category of cloud providers - the neocloud - are among the observable impacts.

Products and services across industries that integrate AI agents dramatically increase their value provided. While the AI frontier expands, clear infrastructure deployment blueprints remain elusive for many. This webinar cuts through the noise, offering our expert perspective on establishing these crucial patterns. The urgency of figuring out how to efficiently deploy and run AI workloads is high because the infrastructure cost of GPU units is significant, but on the other hand ignoring this domain at all leaves more space for competition to step in. The traditional cloud infrastructure providers are offering GPU machines for a premium. Businesses consider the new-gen GPU-focused cloud providers, however the maturity and reliability of neocloud providers is yet to be seen. What are the strategies in order to setup the infrastructure and balance the risks without breaking the bank? Are there proven patterns we can rely on in building the infrastructure for AI? As we are lucky enough to collaborate with the pioneers in the AI domain which positions us well for discussing the possibilities with the audience.

Our experience at the forefront of AI workload orchestration confirms a critical trend: the success of AI integration now heavily leans on advanced distributed systems engineering. We'll share our insights on this evolving dependency. This webinar delivers the context, the expert analysis and implications for distributed systems specialists, for solution designers and business decision makers in general that are figuring out the options for integrating own AI agents reliably, and in efficient manners.