Remove 2010 Remove Computer Science Remove ML
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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning Blog

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. The structured dataset includes order information for products spanning from 2010 to 2017.

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

At its core, Amazon Bedrock provides the foundational infrastructure for robust performance, security, and scalability for deploying machine learning (ML) models. The serverless infrastructure of Amazon Bedrock manages the execution of ML models, resulting in a scalable and reliable application.

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Analyzing the history of Tableau innovation

Tableau

Chris had earned an undergraduate computer science degree from Simon Fraser University and had worked as a database-oriented software engineer. Nov 2010), which allowed users to drag and drop multiple tables on one sheet. Visual encoding is key to explaining ML models to humans. March 2021). Beginning with Tableau 2021.2,

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. About the Authors Greg Benson is a Professor of Computer Science at the University of San Francisco and Chief Scientist at SnapLogic. Clay Elmore is an AI/ML Specialist Solutions Architect at AWS.

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Analyzing the history of Tableau innovation

Tableau

Chris had earned an undergraduate computer science degree from Simon Fraser University and had worked as a database-oriented software engineer. Nov 2010), which allowed users to drag and drop multiple tables on one sheet. Visual encoding is key to explaining ML models to humans. March 2021). Beginning with Tableau 2021.2,

Tableau 98
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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Build verifiable explainability into financial services workflows with Automated Reasoning checks for Amazon Bedrock Guardrails

AWS Machine Learning Blog

Automated Reasoning is a field of computer science focused on mathematical proof and logical deductionsimilar to how an auditor might verify financial statements or how a compliance officer makes sure that regulatory requirements are met. Alfredo has a background in both electrical engineering and computer science.

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