Automating the Model Lifecycle

Automating the Model Lifecycle

Use metrics, checks, and scenarios to schedule jobs and monitor the lifecycle of a data project

About this course

The lifecycle of a data project doesn’t end once a Flow is complete. It’s not possible to Dev/Test a model until it’s perfect because data is ever changing. At the same time, ensuring that your data and models are up-to-date is something that you don’t want to have to do manually. Dataiku DSS has automation features to help with this.

In this course, you will learn the basics of:

  • Scheduling jobs using scenarios
  • Monitoring jobs
  • Monitoring the status and quality of your dataset

 

Curriculum

  • Automation
  • Reporting Scenario Activities
  • Model Lifecycle

About this course

The lifecycle of a data project doesn’t end once a Flow is complete. It’s not possible to Dev/Test a model until it’s perfect because data is ever changing. At the same time, ensuring that your data and models are up-to-date is something that you don’t want to have to do manually. Dataiku DSS has automation features to help with this.

In this course, you will learn the basics of:

  • Scheduling jobs using scenarios
  • Monitoring jobs
  • Monitoring the status and quality of your dataset

 

Curriculum

  • Automation
  • Reporting Scenario Activities
  • Model Lifecycle