AI and machine learning workloads depend on accessing massive datasets to drive model development. However, when project teams attempt to transition pilots to production-level deployments, most discover their existing data architectures struggle to meet the performance demands.
This whitepaper discusses critical architectural considerations for optimizing data access and movement in enterprise-grade AI infrastructure. Discover:
- Common data access bottlenecks that throttle AI project productivity as workloads scale
- Why common approaches like faster storage and NAS/NFS fall short
- How Alluxio serves as a performant and scalable data access layer purpose-built for ML workloads
- Reference architecture on AWS and benchmarks test results