公开(公告)号：US20230342359A1

公开(公告)日：2023-10-26

申请号：US18345789

申请日：2023-06-30

申请人： Microsoft Technology Licensing, LLC

发明人： Irene Rogan SHAFFER ,  Remmelt Herbert Lieve AMMERLAAN ,  Gilbert ANTONIUS ,  Marc T. FRIEDMAN ,  Abhishek ROY ,  Lucas ROSENBLATT ,  Vijay Kumar RAMANI ,  Shi QIAO ,  Alekh JINDAL ,  Peter ORENBERG ,  H M Sajjad Hossain ,  Soundararajan Srinivasan ,  Hiren Shantilal PATEL ,  Markus WEIMER

IPC分类号： G06F16/2453 ,  G06N20/00 ,  G06F11/34 ,  G06F16/901

CPC分类号： G06F16/24542 ,  G06N20/00 ,  G06F11/3466 ,  G06F16/9024

摘要： Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.