Raw data almost always contains errors, inconsistencies, or missing values, requiring thorough preparation, including cleansing and preprocessing techniques such as normalization and feature extraction. Traditionally, this step involved transferring data to local high-performance server storage, which was time-consuming and cumbersome.

VAST’s high-performance capabilities allows direct data processing from shared infrastructure, eliminating the need for data copying. Additionally, we seamlessly integrate with GPU acceleration and NVIDIA RAPIDs, delivering up to 100 times faster results than legacy solutions. This significantly enhances the efficiency and speed of data refinement, making VAST an excellent choice for AI-driven workflows.

Machine learning algorithms learn and identify patterns using refined data during the model training phase. This process includes selecting algorithms, inputting training data, and iteratively adjusting models to reduce errors, all of which demand peak performance levels. Legacy systems require transferring refined data to specialized parallel file storage for processing, resulting in delays and potential system downtime.

VAST Data eliminates the need for copying data and system tuning downtime, allowing AI models to begin training immediately on all of your data. Additionally, VAST creates snapshots of the dataset, recording and preserving everything needed for the model. This streamlined approach ensures efficient and uninterrupted model training, significantly outperforming legacy technologies.

Trained AI models are applied to new, unseen data to generate insights, predictions, or responses. This critical step involves deploying the AI model in real-world scenarios to solve actual problems or answer questions based on previously unencountered data.

With the VAST Data Platform, all interactions—requests and responses—are directly stored on the same platform. This streamlines data capture and facilitates seamless transitions into subsequent data refinement and model training phases, accelerating the continuous improvement cycle of AI models. VAST's unified approach ensures efficient handling of the entire AI workflow, significantly enhancing performance and reducing complexity compared to legacy systems.

For AI systems, it's important to have data provenance and audit trails, especially for legal compliance. These processes ensure that every step in the data lifecycle, from data collection to AI application, is carefully recorded and can be verified. Traditional solutions often need separate databases, like MongoDB or PostgreSQL, to log queries and maintain backups, which can make things more complicated and less efficient.

VAST’s integrated database seamlessly maintains records of prompts and interactions. The VAST Catalog, combined with limitless VAST snapshots, makes organizing and retrieving training datasets easier, ensuring compliance and reproducibility. This allows organizations to show which data was used for model training, improving integrity, and protecting against legal challenges.