Introduction to GenAIOps in Enterprises
Enterprises are adopting Generative AI to help solve many complex use cases with natural language instructions. Building a Gen AI application involves multiple components such as an LLM, data sources, vector store, prompt engineering, and RAG. GenAIOps defines operational best practices for the holistic management of DataOps (Data Operations), LLMOps (Large Language Model Life cycle management), and DevOps (Development and Operations) for building, testing, and deploying generative AI applications.
Challenges in GenAIOps Automation
While pilot projects using Generative AI can start effortlessly, most enterprises need help progressing beyond this phase. According to Everest Research, a staggering 50%+ projects do not move beyond the pilots as they face hurdles due to the absence of established GenAIOps practices. Each step presents unique challenges, from connecting to enterprise data to navigating the complexities of embedding algorithms and managing query phases. These include:
Access to Enterprise Data
This involves creating connectors to various storage solutions and databases, considering different ingestion formats like files, tabular data, or API responses. Unlike traditional ETL, extraction, cleaning, masking, and chunking techniques require special attention, especially when dealing with complex structures like tables in PDFs or removing unwanted HTML tags from web crawls.
