In the ever-evolving landscape of artificial intelligence (AI)-driven research and innovation, the need for high-quality, representative datasets has become paramount. With the advent of Synthetic Tabular Data Generation (STNG) from Cleveland Clinic, AI researchers and data scientists now have a powerful tool at their disposal that has been validated to run on Oracle Cloud Infrastructure (OCI). STNG offers a user-friendly interface designed to swiftly create and assess tabular synthetic data derived from original reference datasets, paving the way for groundbreaking discoveries and cost-effective AI-driven endeavors.
STNG stands as a testament to innovation in AI data generation and evaluation. One of its most remarkable features is its user-friendly interface, which simplifies the otherwise complex task of generating synthetic data. By mimicking the structure and relationships of real data, STNG ensures that the generated datasets are complete, unbiased, and representative. This fidelity to the original data is crucial for maintaining the integrity of AI research findings and ensuring the validity of machine learning (ML) models.
In today’s fast-paced AI research environment, time is of the essence. STNG addresses this challenge by significantly increasing the volume of data generated, typically between 2–5 times more, while simultaneously reducing the time, cost, and risk associated with data collection and processing. This acceleration in data generation can prove invaluable for various aspects of AI research, including model training, fine-tuning, and deployment.
Moreover, the ability to quickly generate diverse and representative datasets can greatly expedite the development of AI applications.
STNG’s multifunction generator is a game-changer for AI researchers. It has the capability to produce eight distinct synthetic datasets, each meticulously validated within a separate AutoML validation process. This validation process encompasses critical AI tasks, such as scaling, feature selection, and hyper-parameter tuning. The result is eight high-quality synthetic datasets ready for use in diverse AI research applications, ensuring that AI projects get off to a flying start.
What sets STNG apart from the competition is its remarkable performance in the realm of AI. In nine head-to-head comparisons with generic synthetic data generators, STNG emerged as the clear winner in eight. STNG outperformed its counterparts, demonstrating its superiority in producing data that mirrors real-world scenarios more accurately. This level of accuracy is pivotal for AI researchers who depend on realistic datasets to train and evaluate ML models.
Scalability and accessibility in AI data generation is important. STNG is at the forefront of these considerations. It has been validated and optimized for deployment in OCI’s global cloud regions by Robert. J. Tomsich Pathology and Laboratory Medicine Institute (PLMI) Center for Artificial Intelligence and Data Science. So, regardless of where you are in the world, STNG is available to fuel your AI data generation needs, ensuring that AI research and innovation can flourish. Further, PMLI is using bare metal GPU compute from Oracle Cloud Infrastructure as it works to accelerate STNG workflows and other technology.
In conclusion, STNG represents a significant advancement in the field of AI data generation and evaluation. Its user-friendly interface, commitment to data fidelity, efficiency gains, multifunctionality, stellar performance in AI applications, and global accessibility make it an indispensable tool for AI researchers and data scientists. As the landscape of AI-driven innovation continues to evolve, STNG stands as a testament to the limitless potential of technology in advancing AI research, discovery, and innovation.
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Dan leads AI Cloud engagements globally for healthcare and life sciences at Oracle, as well as Oracle’s partnership with NVIDIA for HCLS. This aligns a life-long love of cutting-edge technology, with a passion for applying that to help transform healthcare and save lives. To discuss using AI/ML & Accelerated Computing (GPU compute) for your healthcare or life science organization, please email email@example.com.
Dr. Samer Albahra is a renowned pathologist and clinical informaticist, presently serving as a Staff Member at the Cleveland Clinic within the Pathology and Lab Medicine Institute and as an Assistant Professor at Case Western Reserve University. After receiving his MD from Ross University, he undertook post-graduate training in AP/CP Pathology at the University of Texas Health Science Center and in Clinical Informatics at the University of California, Davis.