Artificial Intelligence
Author: Aparajithan Vaidyanathan
Aparajithan Vaidyanathan is a Principal Enterprise Solutions Architect at AWS. He supports enterprise customers migrate and modernize their workloads on AWS cloud. He is a Cloud Architect with 25+ years of experience designing and developing enterprise, large-scale and distributed software systems. He specializes in Generative AI & Machine Learning with focus on moving Enterprise GenAI/ML applications to production, at scale.
How Wipro PARI accelerates PLC code generation using Amazon Bedrock
In this post, we share how Wipro implemented advanced prompt engineering techniques, custom validation logic, and automated code rectification to streamline the development of industrial automation code at scale using Amazon Bedrock. We walk through the architecture along with the key use cases, explain core components and workflows, and share real-world results that show the transformative impact on manufacturing operations.
How Infosys improved accessibility for Event Knowledge using Amazon Nova Pro, Amazon Bedrock and Amazon Elemental Media Services
In this post, we explore how Infosys developed Infosys Event AI to unlock the insights generated from events and conferences. Through its suite of features—including real-time transcription, intelligent summaries, and an interactive chat assistant—Infosys Event AI makes event knowledge accessible and provides an immersive engagement solution for the attendees, during and after the event.
Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone
Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Amazon DataZone allows you to create and manage data zones, which are virtual data lakes that store and process your data, without the need for extensive coding or […]
Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning
In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. Pre-trained language models (PLMs) are undergoing rapid commercial and enterprise adoption in the areas of productivity tools, customer service, search and recommendations, business process automation, and […]