Business-critical infrastructure and services generate massive volumes of observability data from many disparate sources. It can be challenging to synthesize all this data to gain actionable insights for detecting and remediating issues—particularly in the heat of incident response.

Large Language Models (LLMs) can give notoriously inconsistent responses when asked the same question multiple times. For example, if you ask for help writing an Elasticsearch query, sometimes the generated query may be wrapped by an API call, even though we didn’t ask for it. This sometimes subtle, other times dramatic variability adds complexity when integrating generative AI into analyst workflows that expect specifically-formatted responses, like queries.

MLOps stands for Machine Learning Operations. MLOps refers to the set of practices and tools that facilitate the end-to-end lifecycle management of machine learning models, from development and training to deployment and monitoring. The primary objective of MLOps tools is to address the unique challenges associated with deploying and managing machine learning models in real-world scenarios.