TUTORIALS
Introduction
Synthetic data refers to computer-generated information used to enhance or substitute real data, serving to refine AI models, safeguard sensitive information, and address bias concerns.
In today's data-driven landscape, synthetic data has become essential for testing and training AI models. It offers cost-effective production, automatic labeling, and circumvents logistical, ethical, and privacy challenges associated with using real-world data for training deep learning models.
While synthetic data serves as a valuable technique for model alignment, its effectiveness depends on the quality of the generated datasets. AryaXAI provides advanced 'Synthetic AI' techniques such as GPT-2 and CTGAN, enabling the creation of high-quality synthetic datasets.
To know more about synthetic AI functionality in AryaXAI, refer:
- Blog - AryaXAI Synthetics: Delivering the promise of ML observability
- Tutorial - Privacy Preservation in the Age of Synthetic Data - Part I
- Tutorial - Privacy Preservation in the Age of Synthetic Data - Part II
- Case study - AryaXAI Synthetics: Using synthetic ‘AI’ to compliment ‘ML Observability’
Introduction
Synthetic data refers to computer-generated information used to enhance or substitute real data, serving to refine AI models, safeguard sensitive information, and address bias concerns.
In today's data-driven landscape, synthetic data has become essential for testing and training AI models. It offers cost-effective production, automatic labeling, and circumvents logistical, ethical, and privacy challenges associated with using real-world data for training deep learning models.
While synthetic data serves as a valuable technique for model alignment, its effectiveness depends on the quality of the generated datasets. AryaXAI provides advanced 'Synthetic AI' techniques such as GPT-2 and CTGAN, enabling the creation of high-quality synthetic datasets.
To know more about synthetic AI functionality in AryaXAI, refer:
- Blog - AryaXAI Synthetics: Delivering the promise of ML observability
- Tutorial - Privacy Preservation in the Age of Synthetic Data - Part I
- Tutorial - Privacy Preservation in the Age of Synthetic Data - Part II
- Case study - AryaXAI Synthetics: Using synthetic ‘AI’ to compliment ‘ML Observability’
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