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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications. In his spare time, he rides motorcycle and walks with his sheep-a-doodle!

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

About the Authors Dheer Toprani is a System Development Engineer within the Amazon Worldwide Returns and ReCommerce Data Services team. Chaithanya Maisagoni is a Senior Software Development Engineer (AI/ML) in Amazons Worldwide Returns and ReCommerce organization.

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Leveraging Big Data to Fine Tune Customer Experiences

Avaya

Whether you realize it or not, big data is at the heart of practically everything we do today. In today’s smart, digital world, big data has opened the floodgates to never-before-seen possibilities. If you ask us, though, the best customer experiences today are supported by customer journey analytics.

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Generate financial industry-specific insights using generative AI and in-context fine-tuning

AWS Machine Learning

In this blog post, we demonstrate prompt engineering techniques to generate accurate and relevant analysis of tabular data using industry-specific language. This is done by providing large language models (LLMs) in-context sample data with features and labels in the prompt. For certain use cases, fine-tuning may be required.

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Executive Report: The Customer Data Too Often Overlooked by the C-Suite

A recent Calabrio research study of more than 1,000 C-Suite executives has revealed leaders are missing a key data stream – voice of the customer data. Download the report to learn how executives can find and use VoC data to make more informed business decisions.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. However, managing the complex infrastructure required for big data workloads has traditionally been a significant challenge, often requiring specialized expertise.

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning

This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process. One aspect of this data preparation is feature engineering. However, generalizing feature engineering is challenging.