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Deployment stage of the Team Data Science Process lifecycle

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This article outlines the goals, tasks, and deliverables associated with the deployment of the Team Data Science Process (TDSP). This process provides a recommended lifecycle that you can use to structure your data-science projects. The lifecycle outlines the major stages that projects typically execute, often iteratively:

  1. Business understanding
  2. Data acquisition and understanding
  3. Modeling
  4. Deployment
  5. Customer acceptance

Here is a visual representation of the TDSP lifecycle:

TDSP lifecycle

Goal

Deploy models with a data pipeline to a production or production-like environment for final user acceptance.

How to do it

The main task addressed in this stage:

Operationalize the model: Deploy the model and pipeline to a production or production-like environment for application consumption.

Operationalize a model

After you have a set of models that perform well, you can operationalize them for other applications to consume. Depending on the business requirements, predictions are made either in real time or on a batch basis. To deploy models, you expose them with an open API interface. The interface enables the model to be easily consumed from various applications, such as:

  • Online websites
  • Spreadsheets
  • Dashboards
  • Line-of-business applications
  • Back-end applications

For examples of model operationalization with Azure Machine Learning, see Deploy machine learning models to Azure. It is a best practice to build telemetry and monitoring into the production model and the data pipeline that you deploy. This practice helps with subsequent system status reporting and troubleshooting.

Artifacts

  • A status dashboard that displays the system health and key metrics
  • A final modeling report with deployment details
  • A final solution architecture document

Contributors

This article is maintained by Microsoft. It was originally written by the following contributors.

Principal author:

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Next steps

Here are links to each step in the lifecycle of the TDSP:

  1. Business understanding
  2. Data Acquisition and understanding
  3. Modeling
  4. Deployment
  5. Customer acceptance

For Azure, we recommend applying TDSP using Azure Machine Learning: for an overview of Azure Machine Learning see What is Azure Machine Learning?.