Why Agile Methodologies Miss The Mark For AI & ML Projects

  • “Existing methodologies that are either application development-centric or enterprise architecture focused or rooted in hardware or software development approaches face significant challenges when faced with the unique lifecycle requirements of AI projects.
  • Is the deliverable the algorithm, the training data, the model that aggregates them, the code that uses the model for a particular application, all of the above, none of the above? The answer is yes.
  • Responding to the needs for a more iterative approach to data mining and analytics, a consortium of five vendors developed the Cross-industry standard process for data mining (CRISP-DM)”


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