Breaking Down Data Silos to Accelerate AI Workloads for
Data silos greatly inhibit Artificial Intelligence workloads.
They are problematic for several reasons:
- Limited Access to Data
- Inaccurate and Biased Models
- Inefficiency and Redundancy
- Missed Opportunities
- Lack of Data Integration
- Difficulty in Compliance and Data Governance
- Inability to Support Real-time Decision Making
AI tasks require access to the complete dataset for classification or labeling, followed by refining through processes like cleansing and large language model (LLM) training. Each step has different compute and storage performance needs, ranging from slow, inexpensive mass storage to high-performance NVMe storage.
Data owners struggle to accommodate these varying requirements with a single system, balancing lower-performance data classification steps with high-performance GPU training and inferencing on NVMe storage.
Watch this short video with Hammerspace CEO David Flynn and ESG Analyst Scott Sinclair as they discuss the importance of eliminating data silos to simplify AI initiatives.
Learn more about the issues and solutions surrounding data silos in an AI focused world
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Moderated by Hammerspace SVP of Marketing, Molly Presley
Learn more about the issues and solutions surrounding data silos in an AI focused world
Data silos greatly inhibit Artificial Intelligence workloads.
They are problematic for several reasons:
- Limited Access to Data
- Inaccurate and Biased Models
- Inefficiency and Redundancy
- Missed Opportunities
- Lack of Data Integration
- Difficulty in Compliance and Data Governance
- Inability to Support Real-time Decision Making
AI tasks require access to the complete dataset for classification or labeling, followed by refining through processes like cleansing and large language model (LLM) training. Each step has different compute and storage performance needs, ranging from slow, inexpensive mass storage to high-performance NVMe storage.
Data owners struggle to accommodate these varying requirements with a single system, balancing lower-performance data classification steps with high-performance GPU training and inferencing on NVMe storage.
Watch this short video with Hammerspace CEO David Flynn and ESG Analyst Scott Sinclair as they discuss the importance of eliminating data silos to simplify AI initiatives.