Businesses everywhere have engaged in modernization projects with the goal of making their data and application infrastructure more nimble and dynamic. By breaking down monolithic apps into microservices architectures, for example, or making modularized data products, organizations do their best to enable more rapid iterative cycles of design, build, test, and deployment of innovative solutions.

Lost in the talk about OpenAI is the tremendous amount of compute needed to train and fine-tune LLMs, like GPT, and Generative AI, like ChatGPT. Each iteration requires more compute and the limitation imposed by Moore’s Law quickly moves that task from single compute instances to distributed compute. To accomplish this, OpenAI has employed Ray to power the distributed compute platform to train each release of the GPT models.

Data is core to decision making today and organizations often turn to the cloud to build modern data apps for faster access to valuable insights. With cloud operating models, decision making can be accelerated, leading to competitive advantages and increased revenue. Can you achieve similar outcomes with your on-premises data platform? You absolutely can.

Unwelcome … are platform instability, downtime, hardware failure, poor performance, cluster resource contention, repeated process failures, runaway live queries, critical services alarms, invisibility into alarm cacophony… the list goes on. If those are ailments you would like to remedy …