International Business Machines Corporation

Xi'an University of Technology

Yao Dong Liu

Dong Hai Yu

Jiang Bo Kang

Jun Wang

Song Bo

Bo Song

Jing James Xu

Jun Song Wang

Yumei Huang

Xiao Ming Ma

Lei Tian

Lei Gao

Xing Wei

Rui Wang

Yan Zhao Liu

Li Zhang

Ye Hui

Guangpeng Zhang

Feng Gao

Bo Shen

Chuang Gao

One or more computer processors calculate a cache prediction for a received inference request within an inference cache structured as a self-learning tree, wherein the inference request comprises a set of input values. The one or more computer processors responsive to the retrieved cache prediction exceeding a cache prediction threshold, transmit the cache prediction. The one or more computer processors parallel compute a model prediction for the received inference request utilizing a trained model. The one or more computer processors responsive to the retrieved model prediction exceeding a model prediction threshold, convert the trained model into a tree structure. The one or more computer processors update the inference cache with the converted train model. The one or more computer processors transmit the model prediction.

Efficient machine learning model inference

A computer-implemented system, platform, programing product, and/or method for improving transformation selection in an ensemble machine learning (ML) model that includes: providing all base ML models of the ensemble ML model; identifying all of a plurality of Derived Fields in all the base ML models; performing a Derived Field run prediction analysis for all the Derived Fields; computing the Derived Field Importance Weight for Field (DFIW4F) and the Derived Field Importance Weight for Model (DFIW4M) for all the Derived Fields; clustering all the Derived Fields into a plurality of Derived Field clusters, wherein each Derived Field cluster is based upon the DFIW4M and the DFIW4F for the Derived Field; sorting all the Derived Field clusters by best cluster based upon DFIW4M and DFIW4F; and running the base ML models based upon the Derived Fields in the best Derived Field cluster until sufficient base ML models have been run.

System and method for efficient transformation prediction in a data analytics prediction model pipeline

Systems and computer-implemented methods select a subset of methods to generate data schemas for input data from a list of methods for generating data schemas, based on output of a regression model; generate a candidate schema for each method in the subset of methods to generate data schemas; and generate a master data schema for the input data by merging the candidate schema for each method in the subset of methods to generate data schemas, utilizing predetermined rules.

Automated determination of accurate data schema

A method for forecasting server demand includes collecting a historical number of scoring requests from a network using a serverless architecture. A scoring request capacity per server is determined using the historical number of scoring requests. A prediction model predicts a first future value of scoring requests for a first future time span. A current number of servers in a pool of servers handling the scoring requests. Using the prediction model, a determination of whether the current number of servers is capable of handling the first future value of scoring requests for the first future time span is made. Upon determining that the current number of servers is incapable of handling the first future value of scoring requests, one or more additional servers are warmed up. The warmed-up additional servers are added to the pool of servers prior to an arrival of the first future time span.

Efficient serverless method and system of serving artificial intelligence models

Embodiments of the present invention disclose a method, computer program product, and system for estimating the results of a performance test on an updated software application. A method, the method comprising receiving an updated software application, wherein the size of the updated software application is a first size and generating a plurality of small probe, wherein the size of each of the small probe data is a second size, wherein the second size is less than the first size. Conducting a first performance test on the plurality of small probe data and calculating an estimated elapsed time for a performance test on the updated software application. Conducting the performance test on the updated software application and determining if the updated software is given a PASS or FAIL for the performance test, based in part on the elapsed time of the performance test on the updated software application.

Performance evaluation method using simulated probe data mapping

Described are techniques for machine learning library management. The techniques include generating a table including a plurality of machine learning libraries and their current versions that are used in a deployed machine learning platform (MLP) instance, a first available version upgrade for a first machine learning library of the plurality of machine learning libraries, a security indication associated with the first available version upgrade relative to a current version implemented by the first machine learning library, and a compatibility indication between the first available version upgrade and the current version of the first machine learning library. The techniques further include generating a recommendation related to upgrading the first machine learning library based on the security indication and the compatibility indication.

Compatible and secure software upgrades

A computer-implemented method, system and computer program product for accurately identifying an execution time of a performance test. Network latency data is grouped into clustered groups of network latency data. Furthermore, the performance test execution times for the same group of performance tests run in the local and remote cluster environments are obtained. The test execution times impacted by network latency (compensation times) are then determined based on such obtained performance test execution times in the local and remote cluster environments. Such compensation times are then grouped into clustered groups of compensation times. A regression model is built to predict a performance test execution time impacted by network latency (compensation time) using the clustered groups of network latency data and compensation times. The execution time of a performance test run in the remote cluster environment is then generated that takes into consideration the compensation time predicted by the regression model.

Accurately identifying execution time of performance test

A method for instantiating a first specific model, as one record, based on a model scoring template, clustering a plurality of models into a plurality of groups to obtain a plurality of clusters, computing a model contribution weight value for the given cluster, choosing a set of top model(s) from the plurality of models, and updating the model(s) of the set of top model(s) dynamically.

Effective ensemble model prediction system

Solar fountain

A device and a method for testing the rigidity and non-linear relationship of the coupling interface for cylindrical fitting including an axial loading assembly which is provided along a horizontal central line within a side wall of a frame test bench and contacting with the test-piece shaft via a steel ball, a radial loading assembly downward provided along a vertical central line of top board of the frame test bench, a tip of a perpendicular loading part of the radial loading assembly extending into the outer housing of the test-piece housing fixed on an upper plane of a bottom wall of the frame test bench, with the test-piece shaft and the test-piece housing fitted and connected to each other via a coupling interface for cylindrical fitting; a plurality of displacement sensors fixed on the test-piece housing, and test heads of respective displacement sensor assembly aiming at the test-piece shaft.

Device and method for testing rigidity and non-linear relationship of coupling interface for cylindrical fitting

A translocation-simulating loading apparatus for the gear grinding machine with the shaped grinding wheel is provided. The apparatus includes a load-receiving test piece disposed on the gear grinding machine with the shaped grinding wheel and a load-exerting component for use in loading simulation. The gear grinding machine enables linear movements along the X, Y, and Z axes, a rotary movement around the Y axis, a rotary movement C around the Z axis, and a rotary movement A around the X axis. An angle &#x3b1; is formed between the axis L of a ball seat of the load-exerting component and the X axis direction of a Y axis component and an angle formed between the normal line of a load receiving face a and the X direction of the coordinate system of the machine tool is &#x3b1;. A detection method for static stiffness distribution is provided.

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Xi'an, China

Yao Dong Liu