Confessions of a conflicted data scientist: scientific engineering for profit – Wouter Oosterbosch, EMEA, IBM


Session Outline

The data & AI revolution has shown great top-level successes, but all across the globe teams are scratching their heads: why is my model behaving differently in production? Why is my top-level KPI not moving? In this talk, we’ll discuss some of the key lessons learned on how to close the impact gap, and make AI behave…predictably.

Key Takeaways

  • Being data-driven is a good start… but not enough
  • Solid decision making practices are needed to live up to the promise of better-than-human decision making
  • Combining these practices with engineering standards is key for long-term success and impact

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