Data Storage Architectures for Machine Learning and Artificial Intelligence On-Premises and Public Cloud
REPORT Data Storage Architectures for Machine Learning and Artificial Intelligence On-Premises and Public Cloud ENRICO SIGNORETTI TOPICS: ARTIFICIAL INTELLIGENCE DATA STORAGE MACHINE LEARNING Data Storage Architectures for Machine Learning and Artificial Intelligence On-Premises and Public Cloud TABLE OF CONTENTS 1 Summary 2 Market Framework 3 Maturity of Categories 4 Considerations for Selecting ML/AI Storage 5 Vendors to Watch 6 Near-Term Outlook 7 Key Takeaways 8 About Enrico Signoretti 9 About GigaOm 10 Copyright Data Storage Architectures for Machine Learning and Artificial Intelligence 2 1. Summary There is growing interest in machine learning (ML) and artificial intelligence (AI) in enterprise organizations. The market is quickly moving from infrastructures designed for research and development to turn-key solutions that respond quickly to new business requests. ML/AI are strategic technologies across all industries, improving business processes while enhancing the competitiveness of the entire organization. ML/AI software tools are improving and becoming more user-friendly, making it easier to to build new applications or reuse existing models for more use cases. As the ML/AI market matures, high- performance computing (HPC) vendors are now joined by traditional storage manufacturers, that are usually focused on enterprise workloads. Even though the requirements are similar to that of big data analytics workloads, the specific nature of ML/AI algorithms, and GPU-based computing, demand more attention to throughputs and $/GB, primarily because of the sheer amount of data involved in most of the projects. Depending on several factors, including the organization’s strategy, size, security needs, compliance, cost control, flexibility, etc, the infrastructure could be entirely on-premises, in the public cloud, or a combination of both (hybrid) – figure 1.
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