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Build a self-service digital assistant using Amazon Lex and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Solution overview The solution uses Amazon Lex, Amazon Simple Storage Service (Amazon S3), and Amazon Bedrock in the following steps: Users interact with the chatbot through a prebuilt Amazon Lex web UI. The following diagram illustrates the solution architecture and workflow.

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Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

AWS Machine Learning - AI

Amazon SageMaker Studio – It is an integrated development environment (IDE) for machine learning (ML). The following diagram illustrates the solution architecture. The following are the solution workflow steps: Download the product description text and images from the public Amazon Simple Storage Service (Amazon S3) bucket.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases is completely serverless, so you don’t need to manage any infrastructure, and when using Knowledge Bases, you’re only charged for the models, vector databases and storage you use. To learn more about the capabilities of Amazon Bedrock and knowledge bases, refer to Knowledge base for Amazon Bedrock.

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The Future of the Data Lakehouse – Open

Cloudera

These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Data 97
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Five Trends for 2019

Hu's Place - HitachiVantara

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. Meta data will be key, and companies will look to object based storage systems to create a data fabric as a foundation for building large scale flow based data systems.

Trends 86
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Building an Open Data Processing Pipeline for IoT

Cloudera

Last week Cloudera introduced an open end-to-end architecture for IoT and the different components needed to help satisfy today’s enterprise needs regarding operational technology (OT), information technology (IT), data analytics and machine learning (ML), along with modern and traditional application development, deployment, and integration.

IoT 41
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Surveys Show Kubernetes Crossing the Chasm

d2iq

AI and Kubernetes a Perfect Match Another force driving Kubernetes adoption is the rapid maturation of artificial intelligence (AI) and machine learning (ML). One growth area, in particular, Casey notes, is the natural pairing of Kuberntes with artificial intelligence (AI) and machine learning (ML), which he calls a “star duo.”

Survey 87