How Can CPGs Combat ‘The Big Drift’ Putting Their Business At Risk?

Drifting image courtesy of Kevin Miller
  • Detect when a model is starting to drift
  • Test models against the latest real world conditions
  • Replace an outdated model with a better one very quickly
  • First is the complexity of getting models online and offline. How are the data scientists handing models over to ML engineers for testing? How do they manage versioning to deploy the latest and greatest model while undeploying an outdated model, especially if this is one model in part of a larger chain (for example, updating a segmentation model that is part of a larger dynamic pricing model)?
  • Next is the complexity around all the permutations of a single model. A single demand forecasting model can have many permutations per product line per region, with additional versions for testing and optimization. How easy is it to search all models to find the one they’re looking for, check its status, and move it from testing to production?
  • Then there is the complexity of optimizing compute resources across data scientists and use cases. An ML control room enables different data scientists and use cases to share resources instead of standing up dedicated pipelines that can suck up resources even when they aren’t in use.
  • And finally there is ongoing monitoring, testing, and troubleshooting to optimize the performance of live models. A proper ML control room provides full model observability to ensure you have the best performing model in production.

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