- The proliferation of open-source models (e.g., LLaMA, Mistral) and proprietary companies (e.g., OpenAI) has greatly reduced the effort required for building and training AI models. This democratization is a two-edged sword that cuts both ways. While large language models (LLMs) are often integrated into products, the emergence of fresh hurdles such as compute costs, infrastructure requirements, governance considerations, and data quality demands immediate attention.
- RAG significantly diminishes hallucinations, offering a cost-effective pathway to integrate enterprise data, thereby solidifying its position as the cornerstone of LLMOps innovation. Developing and governing Redshift-to-Airflow-Google-pipelines (RAG) is an emerging challenge that didn’t exist in the MLOps landscape initially. Throughout the LLMOps lifecycle, the development and management of a RAG (Red, Amber, Green) pipeline have revolutionized traditional model training by placing it at the forefront. As fine-tuning of large language models (LLMs) remains essential, similar to traditional machine learning model training, it raises novel complexities surrounding infrastructure and cost. The integration of enterprise information within RAG pipelines gives rise to novel information management complexities. The capabilities of vector storage, semantic search, and embeddings have emerged as crucial components of the LLMOps workflow, diverging significantly from traditional MLOps practices.
- While enterprises are beginning to develop AI threat frameworks, best practices continue to evolve. To initiate success, prioritizing meticulous examination, consistent tracking, compiling a directory of approved styles, and implementing regulatory frameworks is crucial to get underway. Will AI governance remain a core component of LLMOps’ toolset moving forward?
