White Papers
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
Explores the transformative capabilities of the Data Access Layer and how it can simplify and accelerate your analytics and AI workloads.
Kevin Petrie, VP of Research at Eckerson Group, shares the following insights in this new research paper:
- The elusive goal of analytics and AI performance
- The architecture of a Data Access Layer in the modern data stack
- The six use cases of the Data Access Layer, including analytics and AI in hybrid environments, workload bursts, cost optimization, migrations and more
- Guiding principles for making your data and AI projects successful
Kevin Petrie
VP of Research
As artificial intelligence continues to transform businesses, getting the most out of AI investments depends on solving the #1 barrier – efficient access to data1.
This technical whitepaper provides you with:
- An in-depth analysis of data access patterns at each stage of the machine learning pipeline – from ingestion to model deployment
- Strategies to optimize data flows in single vs distributed cloud environments
- Real-world examples from leading AI teams at Fintech and Internet companies
- A reference architecture to efficiently serve data to model training and model development with benchmark results
Download it now to level up your AI platform for scalability, mobility and fast data access.
[1] 2021 Gartner AI in Organizations Survey
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As ever more big data computations start to be in-memory, I/O throughput dominates the running times of many workloads. For distributed storage, the read throughput can be improved using caching, however, the write throughput is limited by both disk and network bandwidth due to data replication for fault-tolerance. This paper proposes a new file system architecture to enable frameworks to both read and write reliably at memory speed, by avoiding synchronous data replication on writes.