Sap Se

Ashutosh Patel

Panish Ramakrishna

Nithya Rajagopalan

Hemanth Rajeswari Anantha

Avinash Permude

Prithvi Krishna

Mohammed K A Aehthesham

Ranjith Pavanje Raja Rao

Debashis Banerjee

Rahul Seshagiri Srivatsa

Arjun Swami

Mayank Kamboj

In an example embodiment, a model generation component may additionally assign various cloud resources to a machine learned model so that the training or retraining of the model can be performed using these resource. The containers may be weighted to handle model generation work of different weight. Having one single configuration for a container responsible for generating all models leads to overuse of hardware resources because machine learning algorithms are very resource intensive, and thus dynamically selecting the weight improves hardware utilization.

Dynamically scalable machine learning model generation and retraining through containerization

A computing architecture is optimized for the reuse of machine learning model. A request is received for a machine learning model recommendation that specifies parameters associated with a desired machine learning model (e.g., schema, etc.). Thereafter, a machine learning-based discovery model, recommends at least one machine learning model (to reuse) based on the parameters specified in the request. Next, data characterizing the recommended at least one machine learning model is provided (e.g., loaded into memory, displayed in a graphical user interface, transmitted to a remote computing system, and/or stored in physical persistence, etc.). In some variations, a graphical user interface can be rendered that allows a client application to select one of the recommended machine learning models and, further optionally, to activate the selected machine learning models. Related apparatus, systems, techniques and articles are also described.

Scalable architecture for machine learning model reuse

Various examples are directed to systems and methods for identifying database records in a database table. A database management system receives a search request comprising a first set of strings associated with a first column of the database table and a second set of strings associated with a second column of the database table. The database management system selects a set of first column keywords using the first set of strings and executes a first tier query at the database table. Responsive to determining that no database record returned by the first tier query has a relevance score greater than a threshold value, the database management system executes a second tier query at the database table.

Database record searching with multi-tier queries

A method of processing raw data as it is received from a data provider via an input channel is disclosed. Values are derived from the raw data as it is received from the data provider via the input channel. The derived values correspond to custom fields of a summary table. The summary table is configured to store a summary of the raw data. The custom fields correspond to data capable of improving an analysis of an entity by an analysis tool. The derived values are inserted into the custom fields of the summary table. Access to the summary table is provided as enriched data for use by the analysis tool to improve the analysis of the entity.

Auto derivation of summary data using machine learning

Example embodiments for classification are described. In an example embodiment, a request including a text term to be classified is received from a source system via a communication network at a computer system. A rule associated with the text term is accessed, in which the rule indicates at least one classifier of a plurality of classifiers installed at the computer system. Classification information for the text term is generated at the computer system using the at least one classifier indicated by the rule. The generated classification information includes a classification selected from a taxonomy by the at least one classifier. The generated classification information is transmitted via the communication network to the source system.

Cloud-based pluggable classification system

Example embodiments for augmenting master data schema are described. In an example embodiment, schema of master data to be employed by a plurality of applications are accessed. First additional information is added to the schema, wherein the first additional information is to be employed by each of the plurality of applications in accessing the master data. After the adding of the first additional information, second additional information is added to the schema, wherein the second additional information is to be employed by a first corresponding one of the plurality of applications in accessing the master data. In some example embodiments, third additional information is added to the schema, wherein the third additional information is to be employed by a second corresponding one of the plurality of applications in accessing the master data.

Augmenting database schema using information from multiple sources

Example embodiments for master data synchronization for multiple applications are described. In an example embodiment, master data to be employed by a plurality of applications, as well as schema for the master data, are accessed. One or more publication requests to store the master data and the schema of the master data to a master data store accessible by the plurality of applications are generated. The schema may be cached locally to one or more of the applications.

Growing community of inventors

Bengaluru, India

Ashutosh Patel