AI Development, Data Quality and Data Scarcity - Artificial Intelligence Zone

Search:

DAY

WEEK

MONTH

YEAR

Select your country:
Sign up | Log in

AI Development

Data Quality

Data Scarcity

Data-Centric AI: The Importance of Systematically Engineering Training Data

Unite.AI

SEPTEMBER 12, 2024

Traditionally, AI research and development have focused on refining models, enhancing algorithms, optimizing architectures, and increasing computational power to advance the frontiers of machine learning. However, a noticeable shift is occurring in how experts approach AI development, centered around Data-Centric AI.

Data Quality

Data Quality Data Scarcity AI AI

Synthetic Data: A Model Training Solution

Viso.ai

DECEMBER 18, 2023

Instead of relying on organic events, we generate this data through computer simulations or generative models. Synthetic data can augment existing datasets, create new datasets, or simulate unique scenarios. Specifically, it solves two key problems: data scarcity and privacy concerns. Rapid AI Development.

Computer Vision

Computer Vision Neural Network Auto-complete Data Scarcity

Gretel AI Releases Largest Open Source Text-to-SQL Dataset to Accelerate Artificial Intelligence AI Model Training

Marktechpost

APRIL 4, 2024

Such richness and diversity promise to significantly reduce the time and resources data teams spend on improving data quality, which has traditionally consumed up to 80% of their workload.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence AI Modeling Data Scarcity

Webinars

How to Achieve High-Accuracy Results When Using LLMs

Relevance, Reach, Revenue: How to Turn Marketing Trends From Hype to High-Impact

MORE WEBINARS

Data-Centric AI: The Importance of Systematically Engineering Training Data

Synthetic Data: A Model Training Solution

Gretel AI Releases Largest Open Source Text-to-SQL Dataset to Accelerate Artificial Intelligence AI Model Training

Webinars

Stay Connected