Remove AI Remove Data Preparation Remove Data Quality
article thumbnail

Looking Ahead: The Future of Data Preparation for Generative AI

Data Science Blog

Sponsored Post Generative AI is a significant part of the technology landscape. The effectiveness of generative AI is linked to the data it uses. Similar to how a chef needs fresh ingredients to prepare a meal, generative AI needs well-prepared, clean data to produce outputs.

article thumbnail

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 …

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

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.

article thumbnail

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

Dataconomy

However, an expert in the field says that scaling AI solutions to handle the massive volume of data and real-time demands of large platforms presents a complex set of architectural, data management, and ethical challenges.

article thumbnail

Fine-tuning large language models (LLMs) for 2025

Dataconomy

Granite 3.0 : IBM launched open-source LLMs for enterprise AI 1. Fine-tuning large language models allows businesses to adapt AI to industry-specific needs 2. Data preparation for LLM fine-tuning Proper data preparation is key to achieving high-quality results when fine-tuning LLMs for specific purposes.

article thumbnail

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.

article thumbnail

Training-serving skew

Dataconomy

Transfer learning applications Utilizing transfer learning allows developers to leverage pre-existing models, improving performance in new contexts while minimizing the need for large amounts of data. Skew transformation Data preparation techniques play a vital role in addressing training-serving skew effectively.