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Only 10% of AI projects reached production due to data privacy, bias, and the high computational cost of training.

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  • Data is gathered to create large data stores.

  • These large data stores are used to solve a specific problem using machine learning.

  • The resulting model displays strong generalizability due to the volume of data trained on, and is eventually deployed.

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  • Continuous data collection uses large amounts of communication bandwidth.

  • In privacy-focused applications, the transmission of data may be banned entirely - making model creation impossible.

  • Training large ML models on big data stores is computationally expensive.

    • Traditional centralized training efficiency is limited by single-machine performance.

    • Distributed learning approaches often incur overhead to maintain training performance.

  • Slow training processes lead to long delays between incremental model updates, leading to lack of flexibility in accommodating new data trends.

STADLE could resolve your bottleneck of your data systems.

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