KNIME Continuous Deployment and MLOps

In this course, we will show you how to use the KNIME software to test and deploy a prediction workflow, automate its deployment and enable subsequent continuous deployment, monitoring and maintenance.

Using an example from credit scoring, we will demonstrate how a prediction workflow can be deployed manually, automatically or continuously and how predictions can be generated via a data app or as a REST service.

In the first session of this course, you will learn how to prepare a prediction workflow for deployment. In the second session, you will be introduced to the KNIME Business Hub and learn how to deploy a prediction workflow as a data app or as a REST service. In the third session, you will learn how to use the Continuous Deployment for Data Science (CDDS) framework to enable continuous deployment on KNIME Business Hub. Finally, in the fourth session, you will learn best practices for the production of machine learning models, such as logging and tracking experiments, performance optimization, AutoML and XAI.

• Preparing for Deployment
• Introduction to KNIME Business Hub
• Continuous Deployment for Data Science
• Best Practices when Productionizing Data Science
• Optional follow-up Q&A 

Please note: This course consists of four 75-minute online sessions conducted by a KNIME data scientist and/or a solution engineer. For each session there is an exercise that you can do at home. The solution will be discussed together at the beginning of the next session. On day 5, the course ends with a 15-30 minute closing session.

You should be an advanced KNIME user.

You should already know how to create workflows and components with the KNIME Analytics Platform. Knowledge and experience equivalent to our advanced KNIME Analytics Platform courses (L2 level) is recommended.

You should already have the latest version of the KNIME Analytics Platform installed on your laptop, which you can download here: knime.com/downloads

In this course, you will learn how to integrate and automate KNIME workflows in production environments. You will gain knowledge in applying MLOps practices to efficiently manage the entire lifecycle of machine learning models.