Deployment management offers a concise view of every model, within every project, that has been successfully implemented in your chosen environment.
Before starting the course make sure:
- You are familiar with the preceding sections of the workbench
- You know how to work with other prediction technologies
- Have an understanding of project needs
Keep your mind engaged with these references:
Understanding predictions in practice
After hypotheses come actionable insights. Deployment puts your theories into a production environment, letting machine learning make a difference in people’s lives. Watch your project plans come to life and witness the activity of tested models in a real world setting.
The next step to defining algorithms and model creation is implementation. Deployment answers the question of what next – you’ve built the models using the workbench functionality, now to put them into a production environment so you can actually use them.
Deployment management offers a concise view of every model, within every project, that has been successfully implemented in your chosen environment.