AIOps
Driving step‑function improvements in Service reliability, Dev productivity and Fleet efficiency through Agentic AI research and cross‑stack innovation.
微软研究院博客
In this edition: Privacy enhancements for multiparty deep learning; using smaller, open-source models to provide relevance judgments; new tool uses AI, data to automate innovation and development; Yasuyuki Matsushita named IEEE 2025 Computer Society Fellow.
| Minghua Ma, Gagan Somashekar, Rujia Wang, Chetan Bansal, 和 Saravan Rajmohan
AIOpsLab is an open-source framework designed to evaluate and improve AI agents for cloud operations, offering standardized, scalable benchmarks for real-world testing, enhancing cloud system reliability.
Holistic motion-capture calibration technique without calibration, manual intervention or custom hardware; Research on AI agents for autonomous clouds; Automating proof-oriented program construction; One-to-many testing for natural language code generation.
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Time-series forecasting is a technique used to predict future values based on previously…
In this issue: RELEVANCE automatically evaluates creative LLM responses; Recyclable vitrimer-based printed circuit boards; Lean Attention: Hardware-aware scalable attention mechanism; WaveCoder: a fine-tuned code LLM; New AutoGen training course.
| Anjaly Parayil, Ayush Choure, Fiza Husain, Avi Nayak, Piyali Jana, Rujia Wang, Chetan Bansal, 和 Saravan Rajmohan
Integrating AI into cloud service monitoring improves incident detection accuracy, reduces unnecessary alerts, and enhances overall system reliability. This helps organizations better align with business goals and increase customer satisfaction.
In this issue of Research Focus: Optimized exit-augmented models for scalable efficient inference; NeurIPS LLM Efficiency Challenge; LLM-empowered automated data exploration; Boosting cloud efficiency with data-driven decision-making and optimization.
| Rujia Wang, Chetan Bansal, Supriyo GHOSH, Tom Zimmermann, Xuchao Zhang, 和 Saravan Rajmohan
This research was accepted by the IEEE/ACM International Conference on Software Engineering (ICSE) (opens in new tab), which is a forum for researchers, practitioners, and educators to gather, present, and discuss the most recent innovations, trends, experiences, and issues in…