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Looking Ahead: The Future of Data Preparation for Generative AI

Data Science Blog

Businesses need to understand the trends in data preparation to adapt and succeed. If you input poor-quality data into an AI system, the results will be poor. This principle highlights the need for careful data preparation, ensuring that the input data is accurate, consistent, and relevant.

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Accelerate data preparation for ML in Amazon SageMaker Canvas

AWS Machine Learning Blog

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. Amazon SageMaker Canvas now supports comprehensive data preparation capabilities powered by Amazon SageMaker Data Wrangler.

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Why Is Data Quality Still So Hard to Achieve?

Dataversity

In fact, it’s been more than three decades of innovation in this market, resulting in the development of thousands of data tools and a global data preparation tools market size that’s set […] The post Why Is Data Quality Still So Hard to Achieve? appeared first on DATAVERSITY.

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Augmented analytics

Dataconomy

This technological advancement not only empowers data analysts but also enables non-technical users to engage with data effortlessly, paving the way for enhanced insights and agile strategies. Augmented analytics is the integration of ML and NLP technologies aimed at automating several aspects of data preparation and analysis.

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Fine-tuning large language models (LLMs) for 2025

Dataconomy

Data preparation for LLM fine-tuning Proper data preparation is key to achieving high-quality results when fine-tuning LLMs for specific purposes. Importance of quality data in fine-tuning Data quality is paramount in the fine-tuning process.

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The secret to making data analytics as transformative as generative AI

Flipboard

Presented by SQream The challenges of AI compound as it hurtles forward: demands of data preparation, large data sets and data quality, the time sink of long-running queries, batch processes and more. In this VB Spotlight, William Benton, principal product architect at NVIDIA, and others explain how …

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AI Powers E-Commerce, But Scaling Up Presents Complex Hurdles

Dataconomy

He suggested that a Feature Store can help manage preprocessed data and facilitate cross-team usage, while a centralized Data Warehouse (DWH) domain can unify data preparation and migration. From the data side, this is resolved through centralized data preparation using a DWH (Data Warehouse) domain, Krotkikh said.