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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

To address the problems associated with complex searches, this post describes in detail how you can achieve a search engine that is capable of searching for complex images by integrating Amazon Kendra and Amazon Rekognition. A Python script is used to aid in the process of uploading the datasets and generating the manifest file.

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Generative AI, LLMs and AI Assistants: A Deep Dive into Customer Experience Technology

COPC

Related CX Technology Consulting Fusing technology and expertise to design and deliver exceptional service journeys. Crafting LLM AI Assistants: Roles, Process and Timelines Using the latest AI may seem as easy as developers using APIs in commercial LLM options like OpenAI.

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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning

The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Real-time recommendation inference The inference phase consists of the following steps: The client application makes an inference request to the API gateway.

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The Future of Debt Collection Agencies: Contact Center Technology and Customer-Centric Strategies

NobelBiz

Traditional collection methods, such as persistent phone calls and letters, are making way for more nuanced, technology-driven, and customer-oriented strategies. This evolution reflects broader trends in consumer behavior, regulatory environments, and technological advancements.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Wipro has used the input filter and join functionality of SageMaker batch transformation API.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning

Sportradar is the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on Sportradar knowhow and technology to boost their business.

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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

Amazon SageMaker Feature Store is a purpose-built feature management solution that helps data scientists and ML engineers securely store, discover, and share curated data used in training and prediction workflows. In this example, we ingest records using the FeatureGroup.ingest() API, which ingests records from a Pandas DataFrame.

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