Key Model Deployment Questions
Table of Contents

Questions #

Key questions to answer for successful model deployment include:

  • Which deployment approach will be used: offline or real-time?
  • What is the expected volume of predictions: ten per week, ten per second, etc?
  • How does the system handle spikes in demand?
  • What is the maximum acceptable latency between a prediction request and response service?
  • Where will the model be deployed: cloud, edge device, ordinary server, etc?
  • What kind of network connectivity will be required to transmit prediction requests to the model and predictions to output?
  • How will model drift be detected?
  • How will outliers in data be detected?
  • How robust are the fallback mechanisms?
  • How robust is the model deployment pipeline?
  • Is the training pipeline reproducible?
  • Is the new model better than the old one?
  • Is model explainability required?
  • How much outage (when predictions cannot be served) can the business afford?
  • Will the model need to be updated?
  • Will the history of prediction requests and responses be required in the future, e.g., by government or for further analysis?
  • Will the benefit of operating and maintaining the deployment option outweigh the cost?

References #

  1. Valohai,
  2. Yvonne Cook,

Online references were accessed on 17 May 2022.

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