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Adobe enhances developer productivity using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

This involved creating a pipeline for data ingestion, preprocessing, metadata extraction, and indexing in a vector database. Similarity search and retrieval – The system retrieves the most relevant chunks in the vector database based on similarity scores to the query.

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16 Companies Leading the Way in AI and Data Science

ODSC - Open Data Science

CARTO Since its founding in 2012, CARTO has helped hundreds of thousands of users utilize spatial analytics to improve key business functions such as delivery routes, product/store placements, behavioral marketing, and more.

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Manage your Amazon Lex bot via AWS CloudFormation templates

AWS Machine Learning Blog

It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language. For more information, refer to Enabling custom logic with AWS Lambda functions.

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A comprehensive guide to learning LLMs (Foundational Models)

Mlearning.ai

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build AI/ML solutions.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

in 2012 is now widely referred to as ML’s “Cambrian Explosion.” Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. FP16 is used in deep learning where computational speed is valued, and the lower precision won’t drastically affect the model’s performance.

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Implementing Agents in LangChain

Heartbeat

Here are a few reasons why an agent needs tools: Access to external resources: Tools allow an agent to access and retrieve information from external sources, such as databases, APIs, or web scraping. Hinton is viewed as a leading figure in the deep learning community. Meta's chief A.I. scientist calls A.I.