One of the most critical gaps in traditional Large Language Models (LLMs) is that they rely on static knowledge already contained within them. Basically, they might be very good at understanding and responding to prompts, but they often fall short in providing current or highly specific information.

Large Language Models, or LLMs, have become a near-ubiquitous technology in recent years. Promising the ability to generate human-like content with simple and direct prompts, LLMs have been integrated across a diverse array of systems, purposes, and functions, including content generation, image identification and curation, and even heuristics-based performance testing for APIs and other software components.

You may have wrestled with a web application attempting to call an offsite web service, such as an OpenTelemetry Collector, and gotten an odd error with the word CORS in it. Something like: Or, maybe you got a generic thrown error from your fetch statement that states Error: Failed to fetch …and you wondered, “What’s the problem, and how can I fix it?” These kinds of errors are called CORS errors, and they can be a bit confusing.

In this blog series, we will explore the importance of merging DevOps best practices with MLOps to bridge this gap, enhance an enterprise’s competitive edge, and improve decision-making through data-driven insights. Part one discussed the challenges of separate DevOps and MLOps pipelines and outlined a case for integration.

Today, every enterprise is digital. Regardless of industry, every business must incorporate digital technologies and strategies into its operations to remain competitive. Maintaining reliable IT infrastructures and digital services while minimizing downtime due to unplanned outages is critical to business success.