As organizations try to seize the opportunity, investments in generative AI are on the rise, which will further accelerate the adoption of technologies in this niche.
In this environment, many organizations are bound to end up with a “frankenstein” infrastructure, as teams try out new technologies, experiment, and introduce new capabilities. This has the potential to quickly spiral out of control, exacerbate technical debt, increase upkeep, and drive costs through the roof.
The only tangible way to prevent this from happening is to ensure that organizations are able to securely govern and confidently operate generative AI solutions at scale.
The collection of these processes, guardrails, and integrations is often referred to as MLOps. But generative AI has its own unique challenges, which should be addressed accordingly with what is known as LLMOps, a subset of MLOps, tailored to large language models’ (LLMs) unique challenges and requirements.
