ML Infrastructure

  • A data pipeline for providing training and testing data for models in development
  • A model training pipeline for preparing a model for its intended use
  • A model scoring pipeline to evaluate a trained model against testing data that it has not “seen” previously
  • A post-deployment performance evaluation pipeline to keep track of how well a model is performing in the production environment

ML Ops

  • What modeling frameworks, languages, and tools the team is using; a larger variety makes ML Ops more complicated
  • Testing protocols that define what constitutes adequate testing and the performance standards that must be met before a model can be released in production
  • Change management procedures to release new or updated models in a controlled fashion — and back out changes if needed
  • Post-deployment monitoring to understand unexpected model output “in the wild”

How the Sway AI No-Code Platform Benefits ML Infrastructure and ML Ops

Conclusion

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