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Erschienen in:
2023 | OriginalPaper | Buchkapitel
14. Communication and Deployment
verfasst von : Frank Acito
Erschienen in: Predictive Analytics with KNIME
Verlag: Springer Nature Switzerland
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Abstract
This chapter emphasizes that creating a predictive model is not the final step, but it requires effective communication and deployment to realize its value. Three essential elements of the “endgame” are identified: a final written report, a presentation based on the report, and model deployment. Deployment of models can be challenging, and many models are not successfully deployed due to technical, political, or regulatory issues.
The level of detail in the remaining steps depends on the model’s intended use. The communication process may be straightforward if the model is intended only for internal use by the developers or a small team. However, communication and deployment become more complex and significant if the model is to be integrated into production processes or made available to external stakeholders.
The chapter highlights the importance of written reports and presentations in conveying insights, conclusions, and plans for deploying the model. Clear and effective communication can help decision-makers understand the benefits and potential impact of the model on existing operations. The report and presentation should include a statement of the business problem, the analysis process, a summary of models and findings, deployment plans, and recommendations for further work.
The chapter also covers the complexities of deploying predictive models, ranging from individual use within the organization to real-time processing for external users. The deployment scope affects factors like data privacy, robustness, usability, and maintenance.
In conclusion, the chapter stresses the importance of effective communication, data visualization, and successful deployment to ensure that predictive models deliver value to the organization. It underscores the need to tailor the communication approach to the audience’s needs and to manage model integration complexity for successful deployment.