Creating and Maintaining a Gold Data Set

A Gold Data set in the context of AI typically refers to a high-quality, meticulously curated dataset that  is considered to be of exceptionally high quality and accuracy. 

Having a “Gold Data set” has clear advantages but can be difficult to create, maintain and update without the appropriate data architecture and tools. Without an architecture designed for multiple data sources in multiple locations, organizations face the dilemma of either over-provisioning high-performance storage for all AI phases or paying the “data copy tax” by shuffling file copies between storage silos, increasing time to inference. With data spread across multiple sites or the cloud, this penalty worsens, leading to idle GPUs waiting for data.

Adding to the pain, organizations have significant investments in existing infrastructure, making it costly to replace with a vendor-locked all-purpose storage platform. Rapid AI advancements make it risky to lock into one solution, potentially preventing the adoption of emerging technologies better suited to their use cases.

Whether investing in new infrastructure or dealing with data copy tax complexities and delays, the significant added cost makes it challenging to determine the true ROI for the AI journey.

About Hammerspace:
Hammerspace offers a solution that eliminates the need for over-provisioned, one-size-fits-all storage or manual file shuffling between vendor silos, avoiding the associated “data copy tax.” With its global file system and automated data orchestration across all silos and locations, Hammerspace optimizes AI workloads and adapts to new AI applications. It provides a new paradigm that bridges on-premises silos and cloud, utilizing existing infrastructure from any vendor without compromising results.

Watch this short excerpt from the State of The Data Industry Presentation with Hammerspace CEO David Flynn and ESG Analyst Scott Sinclair discuss the importance of creating and working with gold data sets, and the challenges of a ‘store, copy and merge’ model that creates Frankenstein data sets.
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

A Gold Data set in the context of AI typically refers to a high-quality, meticulously curated dataset that  is considered to be of exceptionally high quality and accuracy. 

Having a “Gold Data set” has clear advantages but can be difficult to create, maintain and update without the appropriate data architecture and tools. Without an architecture designed for multiple data sources in multiple locations, organizations face the dilemma of either over-provisioning high-performance storage for all AI phases or paying the “data copy tax” by shuffling file copies between storage silos, increasing time to inference. With data spread across multiple sites or the cloud, this penalty worsens, leading to idle GPUs waiting for data.

Adding to the pain, organizations have significant investments in existing infrastructure, making it costly to replace with a vendor-locked all-purpose storage platform. Rapid AI advancements make it risky to lock into one solution, potentially preventing the adoption of emerging technologies better suited to their use cases.

Whether investing in new infrastructure or dealing with data copy tax complexities and delays, the significant added cost makes it challenging to determine the true ROI for the AI journey.

About Hammerspace:
Hammerspace offers a solution that eliminates the need for over-provisioned, one-size-fits-all storage or manual file shuffling between vendor silos, avoiding the associated “data copy tax.” With its global file system and automated data orchestration across all silos and locations, Hammerspace optimizes AI workloads and adapts to new AI applications. It provides a new paradigm that bridges on-premises silos and cloud, utilizing existing infrastructure from any vendor without compromising results.

Watch this short excerpt from the State of The Data Industry Presentation with Hammerspace CEO David Flynn and ESG Analyst Scott Sinclair discuss the importance of creating and working with gold data sets, and the challenges of a ‘store, copy and merge’ model that creates Frankenstein data sets.