This article explores TinyML, a branch of machine learning run on microcontrollers like the ESP32. It details how TinyML can be used for local inference, anomaly detection, and efficient data processing with minimal power consumption, using an example of temperature and humidity monitoring.
This article explores how prompt engineering can be used to improve time-series analysis with Large Language Models (LLMs), covering core strategies, preprocessing, anomaly detection, and feature engineering. It provides practical prompts and examples for various tasks.
A lightweight intelligent solution designed to monitor and identify abnormal vibration patterns in real-time. The project details the hardware (XIAO ESP32-S3, Grove Shield, LIS3DHTR accelerometer) and software (SenseCraft AI platform) used to detect vibration anomalies. It explains the GEDAD algorithm used for learning normal vibration patterns and identifying deviations.
PhD student Sarah Alnegheimish is developing Orion, an open-source, user-friendly machine learning framework for detecting anomalies in large-scale industrial and operational settings. She focuses on making machine learning systems accessible, transparent, and trustworthy, and is exploring repurposing pre-trained models for anomaly detection.
Datadog has acquired Metaplane to expand its data observability offerings, particularly for AI applications. Metaplane uses AI-powered anomaly detection and data lineage tracking. The acquisition aims to unify observability across applications and data.
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.
This article explores the use of Isolation Forest for anomaly detection and how SHAP (KernelSHAP and TreeSHAP) can be applied to explain the anomalies detected, providing insights into which features contribute to anomaly scores.
Learn how to use Autoencoders to detect anomalies in time series data in a few lines of code.
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.
