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Strategic Planning for AI and Machine Learning Deployments

  • ScaleFlux 
  • 2 min read

The acceleration of artificial intelligence (AI) and machine learning (ML) usage creates a conundrum for organizations seeking to balance their initiatives to scale their AI and ML capabilities with their initiatives and constraints on energy consumption and sustainability. While Alexander the Great solved his problem with a single slice of his sword, infrastructure and operations leaders have no such option to overcome their Gordian Knot with a single tool and must leverage a combination of tools and strategies.

Optimizing Data Management

  • Data Pruning: Reducing the amount of redundant data through pruning can significantly decrease the computational load.
  • Data Curation: Errors in the training sets, such as incorrect tagging of unstructured data, can lead to errors in the model output which result in lost precision.  In turn, the training may need to use larger data sets and consume more GPU time to achieve the required precision.  Background work to curate the data set, eliminating its errors, can improve efficiency across multiple training runs.




ScaleFlux is the pioneer in deploying Computational Storage at scale. Computational Storage is the foundation for modern, data-driven infrastructure that enables responsive performance, affordable scaling, and agile platforms for compute and storage I/O intensive applications. Founded in 2014, ScaleFlux is a well-funded startup with leaders proven to deploy complex computing and solid-state storage solutions in volume.