Category: Artificial Intelligence

Intelligent document processing (IDP) automates data extraction from diverse document formats, accelerating information retrieval. Manually labeling is expensive and difficult, and Cognizant’s IDP solution on AWS automates document labeling at scale to overcome this challenge. Its customized user interface in Amazon SageMaker Ground Truth lets subject matter experts efficiently label documents.

Large language models like Amazon Bedrock can generate MongoDB queries from natural language questions, transforming how users access NoSQL databases. By leveraging AI and language models, this solution allows business users to query MongoDB data through conversational English instead of code. It connects to MongoDB with PyMongo, generates queries with LangChain and Bedrock, retrieves and formats results into natural language answers.

MLOps applies DevOps principles to machine learning, enabling organizations to efficiently develop, deploy, and manage models at scale. Eviden’s 10-step MLOps assessment examines existing models, establishes governance, creates self-service access, scales data analysis, registers models, enables feature re-use, provides data access, tests models at scale, deploys models, and enables API access. This end-to-end approach streamlines model creation and deployment while ensuring governance and consistency.

Healthcare organizations have vast amounts of valuable but siloed data. A new solution from AWS Partners Redox, Cloudwick, and ClearDATA helps healthcare customers use AWS HealthLake to extract value from their data. Redox integrates and translates data into AWS, while ClearDATA provides 24/7 security and compliance, and Cloudwick Amorphic enables teams to quickly build analytics and workflows to improve care.

Vcinity’s data movement and remote data access solutions enable enterprises to build continuous data pipelines that provide secure, performant access to distributed data. By extending high-speed networking protocols over wide area networks, Vcinity allows AWS services to operate on remote data as if it were local, reducing data transfer costs and latency. This enables real-time analytics, AI/ML model training, cloud migrations, and other use cases.

Verana Health leverages an exclusive real-world data network and AI-enhanced data engine to transform healthcare data into curated, disease-specific data modules called Qdata. This powers Verana’s analytics solutions for real-world evidence generation, clinical trials, quality reporting, and registry data management to enhance patient care and quality of life. Through AWS Data Exchange and Amazon Redshift, Verana offers life sciences customers easy, convenient access to high-quality clinical real-world data for research.

Many startups want to use machine learning but struggle with developing scalable MLOps pipelines. Automat-IT’s MLOps Accelerator helps startups fast-track their machine learning journey and provides an end-to-end automated solution for the ML lifecycle, from data preparation to deployment, leveraging AWS services. With customizable pipelines and dedicated ML experts, Automat-IT empowers various roles to develop, operationalize, and monitor models efficiently.

Rapyder’s Call Agent Analyzer uses generative AI on AWS to revolutionize call center operations. It efficiently processes multilingual audio, summarizes calls, analyzes script adherence, and structures insights into actionable data. This solution helps businesses enhance customer satisfaction through data-driven call agent performance evaluation and training. As an AWS Partner, Rapyder provides cutting-edge cloud solutions that are reshaping industries like customer service.

Infosys is leveraging AWS generative AI capabilities like Amazon Bedrock and AWS Trainium to enhance its enterprise solutions. For example, Infosys Personalized Smart Video uses Amazon Bedrock to create rich, dynamic video content, while Infosys Cortex applies generative AI to analyze call transcripts and improve customer engagement. Overall, Infosys is rapidly adopting AWS’ flexible and scalable generative AI services to boost automation, productivity and user experience across its portfolio.

Darwinbox wanted to reduce the time to infer resumes against job descriptions using PyTorch models. AWS Premier Partner Minfy helped them leverage Amazon SageMaker and AWS Inferentia to compile models with Neuron SDK and deploy them, achieving 87% faster inference without retraining. Key steps were compiling models with the Neuron SDK, extending SageMaker containers, using Inference Recommender to optimize configurations, and sending requests in mini-batches.