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Why data quality is critical for marketing in the age of GenAI

AI News

Without that, the AI falls flat, leaving marketers grappling with a less-than-magical reality. AI-powered marketing fail Let’s take a closer look at what AI-powered marketing with poor data quality could look like. I’m excited to use the personal shopper AI to give me an experience that’s easy and customised to me.

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How to build a successful AI strategy

IBM Journey to AI blog

By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, from manufacturing to hospitality, healthcare and academia. Without an AI strategy, organizations risk missing out on the benefits AI can offer. What is an AI strategy?

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AWS’ Generative AI Strategy Starts to Take Shape and Looks a Lot Like Microsoft’s

TheSequence

📝 Editorial: AWS’ Generative AI Strategy Starts to Take Shape and Looks a Lot Like Microsoft’s The AWS re:Invent conference has long been regarded as the premier event of the year for cloud computing. Bedrock has emerged as the cornerstone of AWS's generative AI strategy, now supporting Anthropic’s Claude 2.1

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AI Bias & Cultural Stereotypes: Effects, Limitations, & Mitigation

Unite.AI

Hence, it is vital to rapidly minimize issues present in Generative AI technologies. Several key strategies can be implemented to reduce bias in AI models. Some of these are: Ensure Data Quality: Ingesting complete, accurate, and clean data into an AI model can help reduce bias and produce more accurate results.

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

Monitoring – Continuous surveillance completes checks for drifts related to data quality, model quality, and feature attribution. Workflow A corresponds to preprocessing, data quality and feature attribution drift checks, inference, and postprocessing. Workflow B corresponds to model quality drift checks.

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LXT’s Report ‘The Path to AI Maturity 2024’: Unmasking the Future of AI Innovation and Corporate Transformation

Unite.AI

The demand for high-quality training data is intensifying , with 66% of respondents anticipating an increase in their training data needs over the next two to five years. This underscores the critical role of data in training more sophisticated and accurate AI models.

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Announcing the 2024 Data Engineering & Ai X Innovation Summits

ODSC - Open Data Science

Join us in the city of Boston on April 24th for a full day of talks on a wide range of topics, including Data Engineering, Machine Learning, Cloud Data Services, Big Data Services, Data Pipelines and Integration, Monitoring and Management, Data Quality and Governance, and Data Exploration.