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How we built our AI Lakehouse

AssemblyAI

This rapid growth in data volume has introduced a new set of challenges, from managing organizational overhead to ensuring the utility and accessibility of our vast datasets. Each team has its own unique data needs and workflows, contributing to an increasingly complex and diverse data ecosystem.

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Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI

Flipboard

Building a reliable application with guardrails and HITL To comply with HIPAA standards and provide patient privacy, we implemented strict data governance practices alongside a hybrid approach that combines fine-tuned LLMs using Amazon Bedrock APIs with Retrieval Augmented Generation (RAG) using Amazon OpenSearch Service.

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Data Intelligence empowers informed decisions

Pickl AI

Exploring technologies like Data visualization tools and predictive modeling becomes our compass in this intricate landscape. Data governance and security Like a fortress protecting its treasures, data governance, and security form the stronghold of practical Data Intelligence.

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Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

AWS Machine Learning Blog

Agmatix’s technology architecture is built on AWS. Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. AWS Glue accesses data from Amazon S3 to perform data quality checks and important transformations.

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