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Showing posts with the label synthetic data ai

Onix Kingfisher – Powering AI Innovation with Synthetic Data

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  Enabling Secure AI Development with Onix Kingfisher Modern enterprises need reliable, high-quality data to train AI models, validate applications, and accelerate continuous testing. However, using real production data creates challenges around privacy, compliance, and accessibility. Onix Kingfisher addresses these challenges as an advanced AI synthetic data solution that generates realistic, production-like datasets without exposing sensitive information. By enabling organizations to create secure and scalable synthetic datasets, Kingfisher helps businesses accelerate AI adoption while maintaining governance and data protection standards. Generates realistic synthetic datasets without PII exposure Supports AI model training and continuous testing Enables secure data access across development environments Accelerating Continuous Testing with AI Synthetic Data Traditional synthetic data tools often rely on rigid rules that fail to replicate real-world complexity. Onix...

Gartner Says 60%+ of AI Data Will Be Synthetic - Here's What That Means

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Few statistics have shaped the conversation around artificial intelligence as much as this one: Gartner has predicted that 60% of the data used to develop AI and analytics projects would be synthetically generated by 2024, up from just 1% in 2021. That's not incremental change. That's a near-total reversal of how enterprises source the data that powers their models, and it happened in the span of a few years. So what does this shift toward synthetic data AI actually mean for your business? Let's unpack it. The problem: AI is outgrowing the data that feeds it Synthetic data is artificially generated information that mimics the statistical patterns, relationships, and structure of real-world data, without containing any actual records. Its explosive rise isn't hype; it's a response to three problems that every data-driven enterprise is now facing at once. AI is starving for data. Modern machine learning models need vast, diverse, high-quality datasets to perform well....