---
title: Key Model Deployment Questions
description: "## 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…"
url: "https://sdscope.com/doc/model-deployment-2/"
updated: "2022-06-03"
category: 10. Model Deployment
---

# Key Model Deployment Questions

## 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, https://valohai.com/model-deployment
2. Yvonne Cook, https://www.itproportal.com/features/overcoming-the-challenges-of-machine-learning-modeldeployment

***Online references were accessed on 17 May 2022**.*

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