Busting the ROI Myth: Deploying ML @ Scale - Ahmed Khamassi, Equinor

In most traditional and digital insudtries, the application of AI/ML use case by use case may not lead to exciting business outcomes that justify the cost.

In this presentation, I detail how we are creating a ML products churning organisation that can deliver both value and innovation at scale.

Our approach busts the myth of ROI as is traditionally applied to ML.

KEY TAKEAWAYS

  • Cutting edge techniques enable the design of ML architectures and processes that can be applied to a class of problems rather than a specific use case
  • Building service based data science teams will not generate the value and innovation businesses exepct from the field
  • Building platform teams that deliver core scalable technologies that are supported by cutting edge architectures and methods is what we advocate
  • This requires change in the way organisations in traditional businesses approach and think about technology

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