![[L3-CD] Continuous Deployment and MLOps - Online](/sites/default/files/styles/1024w/public/2020-12/%5BL3-PC%5D%20KNIME%20Server%20Course%20Productionizing%20and%20Collaboration%20-%20Online.png?itok=rWgM5w7S "[L3-CD] Continuous Deployment and MLOps - Online")
You have created and trained a machine learning model with KNIME Analytics Platform. But how to put it into production so that it can produce predictions? In this course, we will show you how to use KNIME Software to test and deploy a prediction workflow, automate its deployment and enable the subsequent continuous deployments, monitoring, and maintenance.
We will use a credit scoring use case as an example to demonstrate how to deploy a prediction workflow manually, automatically, or continuously, and how to generate predictions 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 KNIME Business Hub and will learn how to deploy a prediction workflow as a data app or as a REST service. Next, 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 about monitoring machine learning models, and about the best practices to productionize machine learning models, such as, experiment logging and tracking, performance optimization, AutoML, and XAI.
This is an instructor-led course consisting of four, 75-minutes online sessions run by our KNIME data scientists and solution engineers. Each session has an exercise for you to complete at home and together, we will go through the solution at the start of the following session. The course concludes with a 15 to 30-minute wrap up session.
Course Content:
- Session 1: Preparing for Deployment
- Session 2: Introduction to KNIME Business Hub
- Session 3: Continuous Deployment for Data Science
- Session 4: Best Practices when Productionizing Data Science
- Session 5: Optional follow-up Q&A (15-30min)
FAQ
You first need to create an account on the KNIME Learning Store. After you log on to the KNIME Learning Store, clicking on the “Register now” button will take you to the course web page.
You should already know how to build workflows, access databases and files, use flow variables, train machine learning models, and be familiar with REST services and components in KNIME Analytics Platform. We recommend taking L1-DS and L2-DS courses before attending this course.
You can join the course using the Zoom links found in your LearnUpon course page. Please note that each Zoom link is specific to a particular session. Make sure you have a stable internet connection!
Sure! The sessions will be recorded and you’ll have access to each one for one month from the time the session is over.
Absolutely - fire away!
Your own laptop, ideally pre-installed with the latest version of KNIME Analytics Platform and required extensions (details will follow in the reminder email).
Download the latest free, open source version of KNIME Analytics Platform here: knime.com/download.
You will be granted temporary access to KNIME Business Hub during this course to work on exercises. The credential to access KNIME Business Hub will be given to you on the first day of the course. You do not need to use your organization’s KNIME Business Hub for this course.
Check out our Self-paced courses, KNIME Books, Cheat Sheets, KNIME Forum, and KNIME Hub.
![[L4-ML] Introduction to Machine Learning Algorithms - Online](/sites/default/files/styles/295x205/public/2020-05/knime-austin-109_lowres_0.jpg?itok=Jck_cI3l "[L4-ML] Introduction to Machine Learning Algorithms - Online")
