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Traditionally, transforming raw data into actionableintelligence has demanded significant engineering effort. It often requires managing multiple machine learning (ML) models, designing complex workflows, and integrating diverse data sources into production-ready formats.
Success comes from methodically using techniques like promptengineering and chunking to improve both the retrieval and generation stages of RAG. About the Authors Clement Perrot is a Senior Solutions Architect and AI/ML Specialist at AWS, where he helps early-stage startups build and use AI on the AWS platform.
There are various technologies that help operationalize and optimize the process of field trials, including data management and analytics, IoT, remote sensing, robotics, machine learning (ML), and now generative AI. The transformed data acts as the input to AI/ML services. AWS Lambda is then used to further enrich the data.
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