Heena Thakur

Sia

With the advent of OpenAI's ChatGPT, Large Language Models (LLMs) have become a hotbed of innovation in the AI domain. These advanced deep learning models have the ability to generate human-like text, making them highly effective for a variety of tasks, such as powering conversational AI, providing writing assistance, and aiding in programming tasks. However, the operationalization of LLM-driven applications in a production environment brings with it a unique set of challenges. These hurdles have given rise to the concept of "LLMOps", a term coined to describe the suite of tools and best practices employed to manage the lifecycle of LLM-based applications, spanning from development and deployment to ongoing maintenance. While LLMOps is a subset of MLOps, it involves a distinct set of steps in the creation of an LLM-powered application, differing significantly from the processes used in building applications based on traditional machine learning models. Contrary to training an LLM from ground zero, the emphasis is placed on adapting pre-trained LLMs for specific downstream tasks. This process entails selecting an appropriate foundational model, employing LLMs for task-specific applications, evaluating their performance, and eventually deploying and monitoring the models in real-world scenarios. Though LLMOps is a relatively new sector within the AI industry, it is poised for significant growth and evolution as the use of LLMs becomes more widespread. In summary, the emergence and growth of LLMs and LLMOps signify a major shift in the development and management of AI-driven products. Sia is an AI platform that utilizes proprietary datasets to train ChatGPT-like models, creating intelligent applications capable of sophisticated interactions within a business ecosystem.