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The Rise and Fall of Data Science Trends: A 2018–2024 Conference Perspective

ODSC - Open Data Science

The next wave of advancements, including fine-tuned LLMs and multimodal AI, has enabled creative applications in content creation, coding assistance, and conversational agents. However, with this growth came concerns around misinformation, ethical AI usage, and data privacy, fueling discussions around responsible AI deployment.

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Bring your own AI using Amazon SageMaker with Salesforce Data Cloud

AWS Machine Learning Blog

As a result, businesses can accelerate time to market while maintaining data integrity and security, and reduce the operational burden of moving data from one location to another. With Einstein Studio, a gateway to AI tools on the data platform, admins and data scientists can effortlessly create models with a few clicks or using code.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning Blog

By following these guidelines, organizations can follow responsible AI best practices for creating high-quality ground truth datasets for deterministic evaluation of question-answering assistants. Rahul Jani is a Data Architect with AWS Professional Service.

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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning Blog

From internal knowledge bases for customer support to external conversational AI assistants, these applications use LLMs to provide human-like responses to natural language queries. We encourage you to adopt these best practices and start evaluating your generative AI question answering pipelines with the FMEval toolkit today.