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For supreme AI data management, the AI data lifecycle needs a clear, continuous flow, both for training of AI models, and for applying AI models to real time decision making.
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.
AI systems are data-centric and each application may need a unique data management workflow to meet business requirements across multiple stages. Diverse data types, throughput needs, and the level of support for different compliance regimes vary across these phases.
The typical data journey in a single AI project might include the following:
Each of these pipeline stages has a unique set of requirements for capacity, throughput, and latency – and could each represent a potential bottleneck in the AI pipeline, delaying data delivery for the next step in the process. A common mistake is to treat each stage as a separate silo – each optimized to meet the characteristics of that stage. However, this often overlooks the most common challenge shared by each stage – the need for continuous and seamless data f low through the pipeline. Without this end-to-end approach, the individual silos tend to work in batch mode, with no continuity and frequent data stalls, failing to deliver an efficient and frictionless data journey from one stage to the next and onward.
While scalable, high-performance file services are essential for most AI workloads, support for other workloads is also required. As organizations mature their development processes and start to bring AI into more areas of the business, several supporting systems become increasingly important. In addition, the evolution of AI environments introduces additional storage requirements for AI data management, such as:
Planning each of these additional demands in the overall AI data lifecycle context is essential. While it may be possible to identify unique storage architectures to optimize each style of workload discussed above, it is vital to carefully orchestrate the entire AI data journey to deliver continuous 24×7 data operations.
Business and Technical leadership are essential for success in AI projects. It is also crucial to manage data through the entire lifecycle to ensure a streamlined data pipeline and assist operations and governance, including compliance, audit trails, and process knowledge.
Here are five critical considerations for AI data management: