Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents occurring within these systems can lead to service disruptions and adversely affect user experience. To swiftly resolve such incidents, on-call engineers depend on crafting domain-specific language (DSL) queries to analyze telemetry data. However, writing these queries can be challenging and time-consuming. This paper presents a thorough empirical study on the utilization of queries of KQL, a DSL employed for incident management in a large-scale cloud management system at Microsoft. The findings obtained underscore the importance and viability of KQL queries recommendation to enhance incident management. Building upon these valuable insights, we introduce Xpert, an end-to-end machine learning framework that automates KQL recommendation process. By leveraging historical incident data and large language models, Xpert generates customized KQL queries tailored to new incidents. Furthermore, Xpert incorporates a novel performance metric called Xcore, enabling a thorough evaluation of query quality from three comprehensive perspectives. We conduct extensive evaluations of Xpert, demonstrating its effectiveness in offline settings. Notably, we deploy Xpert in the real production environment of a large-scale incident management system in Microsoft, validating its efficiency in supporting incident management. To the best of our knowledge, this paper represents the first empirical study of its kind, and Xpert stands as a pioneering DSL query recommendation framework designed for incident management.

XMOS (Bristol, UK) has launched an all-new software development kit (SDK) for its xcore.ai The SDK includes TensorFlowLite for Microcontroller development tools and provides standardised tools and resources that they need to create devices that absorb contextual data from their environment, infer meaning from that data, and translate the results into action. Scripts, tools and libraries to convert TensorFlowLite for Microcontroller models into a format that targets accelerated operations on the xcore.ai The SDK includes examples showing a variety of operations based on bare-metal and FreeRTOS operation, including smart microphone sensing, as well as guides, example builds and execution walkthroughs, and access to XMOS' open-source libraries of interfaces & signal processing algorithms These tools will enable developers to rapidly deploy custom or off-the-shelf AI models using a standard framework alongside all of the control, communications, signal and I/O processing required to create a complete and secure application solution. "Our AIoT SDK enables developers to create intelligent endpoint-AI solutions for a huge variety of applications," said Mark Lippett, CEO of XMOS.

Artificial intelligence (AI), digital signal processing (DSP), control and I/O are delivered in a single device, the xcoer.ai, Single device prices for the xcore.ai It can be used by electronics manufacturers to integrate high-performance processing and intelligence economically into products. According to XMOS, xcore.ai is a new generation of embedded platform. It has fast processing and neural network capabilities to enable data to be processed locally and actions taken on-device within nanoseconds. It can interpret data without communication with the cloud and delivers the performance of an applications processor with the ease-of-use of a microcontroller, enabling embedded software engineers to deploy every different class of processing workload on a single multi-core crossover processor, says XMOS.