One example, the VAST Data Platform, offers unified storage, database, and data-driven function engine services built for AI, enabling seamless access and retrieval of data essential for AI model development and training. With enterprise-grade security and compliance features, the platform can capture, catalog, refine, enrich, and preserve data through real-time deep data analysis and learning to ensure optimal resource utilization for faster processing, maximizing the efficiency and speed of AI workflows across all stages of a data pipeline.
Hybrid and multicloud strategies
It can be tempting to pick a single hyperscaler and use the cloud-based architecture they provide, effectively “throwing money at the problem.” Yet, to achieve the level of adaptability and performance required to build an AI program and grow it, many organizations are choosing to embrace hybrid and multicloud strategies. By leveraging a combination of on-premises, private cloud, and public cloud resources, businesses can optimize their infrastructure to meet specific performance and cost requirements, while garnering the flexibility required to deliver value from data as fast as the market demands it. This approach ensures that sensitive data can be securely processed on-premises while taking advantage of the scalability and advanced services offered by public cloud providers for AI workloads, thus maintaining high compute performance and efficient data processing.
Embracing edge computing
As AI applications increasingly demand real-time processing and low-latency responses, incorporating edge computing into the data architecture is becoming essential. By processing data closer to the source, edge computing reduces latency and bandwidth usage, enabling faster decision-making and improved user experiences. This is particularly relevant for IoT and other applications where immediate insights are critical, ensuring that the performance of the AI pipeline remains high even in distributed environments.