Chapter 1. Overview of developing a data model
Read this section to understand the work required to develop and deploy an application that uses a predictive model created using Red Hat OpenShift Data Science.
Your organization might split responsibility for this process between several roles, such as a data scientist and an application developer, or this work might be done by a single role. An appropriate role is noted for each step.
Table 1.1. Development tasks by role
| Application developer | Data scientist | Task description |
|---|---|---|
| ✔ | Create a Python S2I project in Git using an OpenShift Data Science application template.
| |
| ✔ | Configure user access to the Git project so that data scientists can push to and pull from the repository. | |
| From this point, you can develop the model and the application that uses it simultaneously. | ||
| ✔ | Create an OpenShift application using the project repository. | |
| ✔ | Build the OpenShift application to verify your code. | |
| ✔ | ||
| ✔ | Launch Jupyter and either create or import a notebook. | |
| ✔ | Import the application Git project into JupyterLab. | |
| ✔ | Develop and test your model using notebooks in JupyterLab. | |
| ✔ | Save your model as an independent Python function in a separate Python file. | |
| ✔ |
Update the | |
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| ✔ | ✔ | |