Tata Consultancy Services Limited

Mayank Mishra

Rekha Singhal

Shruti Kunde

Dheeraj Chahal

Balamuralidhar Purushothaman

Gautam Shroff

Jayavardhana Rama Gubbi Lakshminarasimha

Manoj Karunakara Nambiar

Sreehari Kumar Bhogineni

Rishin Raj

Shripad Salsingikar

Sunil D Joshi

Vishnu Hariharan Anand

Vishal Bajpai

Mahesh Rangarajan

Gopi Kandaswamy

Manju Ramesh

Archisman Bhowmick

Jegan Mohan Ponraj

Srinivas Kotamraju

Vaibhav Vyawahare

Sunil Dattatraya Joshi

Mahesh Rangaraj

Rama Gubbi Lakshminarasimha Jayavardhana

Anand Vishnu Hariharan

The present disclosure provides a scalable acceleration of data processing in Machine Learning pipeline which is unavailable in conventional methods. Initially, the system receives a dataset and a data processing code. A plurality of sample datasets are obtained based on the received dataset using a sampling technique. A plurality of performance parameters corresponding to each of the plurality of sample datasets are obtained based on the data processing code using a profiling technique. A plurality of scalable performance parameters corresponding to each of a plurality of larger datasets are predicted based on the plurality of performance parameters and the data processing code using a curve fitting technique. Simultaneously, a plurality of anti-patterns are located in the data processing code using a pattern matching technique. Finally, an accelerated code is recommended based on the plurality of anti-patterns and the predicted plurality of scalable performance parameters using an accelerated code recommendation technique.

Method and system for scalable acceleration of data processing pipeline

This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.

Efficient deployment of machine learning and deep learning model's pipeline for serving service level agreement

This disclosure relates generally to accelerating development and deployment of enterprise applications where the applications involve both data driven and task driven components in data driven enterprise information technology (IT) systems. The disclosed system is capable of determining components of the application that may be task-driven and/or those components which may be data-driven using inputs such as business use case, data sources and requirements specifications. The system is capable of determining the components that may be developed using task-driven and data-drive paradigms and enables migration of components from the task driven paradigm to the data driven paradigm. Also, the system trains a reinforcement learning (RL) model for facilitating migration of the identified components from the task driven paradigm to the data driven paradigm. The system is further capable of integrating the migrated and existing components to accelerate development and deployment an integrated IT application.

Accelerating development and deployment of enterprise applications in data driven enterprise IT systems

This disclosure relates generally to systems and methods for data acquisition and asset inspection in presence of magnetic interference. Data acquisition and assets inspection systems in many infrastructures such as railway, power line, and bridges provide inaccurate results in presence of magnetic interference. The proposed system and method proposes UAV based navigation through a dynamic correction path to inspect one or more assets in one or more infrastructures. A plurality of sensors are integrated with the UAV to acquire images of the one or more assets in presence of magnetic field. The acquired images are further processed to segment and detect anomalies in one or more parts of the one or more assets. The detected anomalies are further classified as potential anomalies and non-potential anomalies. The proposed method provides accurate results with reduced processing time.

Systems and methods for data acquisition and asset inspection in presence of magnetic interference

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Thane, India

Mayank Mishra