Sawmills AI has introduced a smart telemetry data management platform aimed at reducing costs and improving data quality for enterprise observability. By acting as a middleware layer that uses AI and ML to optimize telemetry data before it reaches vendors like Datadog and Splunk, Sawmills helps companies manage data efficiently, retain data sovereignty, and reduce unnecessary data processing costs.
Article discusses a study at MIT Data to AI Lab comparing large language models (LLMs) with other methods for detecting anomalies in time series data. Despite losing to other methods, LLMs show potential for zero-shot learning and direct integration in deployment, offering efficiency gains.
MIT researchers have developed a framework using large language models (LLMs) to efficiently detect anomalies in time-series data from complex systems like wind farms or satellites, potentially flagging problems before they occur.
MIT researchers have developed a method using large language models to detect anomalies in complex systems without the need for training. The approach, called SigLLM, converts time-series data into text-based inputs for the language model to process. Two anomaly detection approaches, Prompter and Detector, were developed and showed promising results in initial tests.