Remove Big Data Remove Data Ingestion Remove Metadata
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Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

AI News

Ahead of AI & Big Data Expo Europe , Han Heloir, EMEA gen AI senior solutions architect at MongoDB , discusses the future of AI-powered applications and the role of scalable databases in supporting generative AI and enhancing business processes. Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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A Beginner’s Guide to Data Warehousing

Unite.AI

In this digital economy, data is paramount. Today, all sectors, from private enterprises to public entities, use big data to make critical business decisions. However, the data ecosystem faces numerous challenges regarding large data volume, variety, and velocity. Enter data warehousing!

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Data architecture strategy for data quality

IBM Journey to AI blog

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock

AWS Machine Learning Blog

This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock. Twilio’s use case Twilio wanted to provide an AI assistant to help their data analysts find data in their data lake.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning Blog

Additionally, you can enable model invocation logging to collect invocation logs, full request response data, and metadata for all Amazon Bedrock model API invocations in your AWS account. Tanvi Singhal is a Data Scientist within AWS Professional Services.

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning Blog

A feature store maintains user profile data. A media metadata store keeps the promotion movie list up to date. A language model takes the current movie list and user profile data, and outputs the top three recommended movies for each user, written in their preferred tone.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.