Streamlining AI Data Management: Five Recommendations to Optimize Data Pipelines

Artificial Intelligence (AI) involves several different stages from data ingest to model training, inference, and more. Each phase can become a bottleneck in the process, slowing the time to build new models or deliver new insights.

Increasingly disparate data sources and model complexity magnify pipeline bottlenecks, so it is not surprising that many AI projects take too long, go over budget, or fail to reach production.

AI data management across the data lifecycle and capable AI storage are crucial to improving the efficiency of AI systems. Certainly, a model-centric approach for AI can benefit from increasing the quantity of training data and enhancing the quality and diversity of the data used for learning, so building a data-centric pipeline to manage the end-to-end data lifecycle is critical.

This whitepaper examines common issues and considerations in AI data management, and the data journey in AI workloads, and explores how to address these issues using a comprehensive AI data management system.