Build Your Own Recommender System
Hands-On Workshop
Build Your Own Recommender System
April 26th, 2022 @ 11am - 1pm ET (8am - 10am PT)
*Limited spots available
Recommendation engines are relevant in many industries. Media and video companies who would like to increase viewing engagement, banking and financial services that would like to promote personalization of financial products to their customers and of course, retail and ecommerce to boost growth and increase revenue.
Start building your first recommender application with cnvrg.io AI Blueprints in this hands-on workshop. We’ll introduce AI Blueprints, and we’ll take a look at the Flows feature in cnvrg.io on which Blueprints is built. When you leave this workshop, you’ll have built a system that can make simple recommendations without ever having to train a model. To keep instruction focused space will be a limited number of participants, so register and save your place today!
- How to build and deploy an end-to-end recommendation system
- How to connect your data to AI blueprints for building a recommendation system
- What are different recommendation system use cases that you can apply to help your business
- How to apply recommendation systems to your specific use case
- Anyone that completes this course will earn a Recommendation System Certificate and badge that can be shared on Linkedin, Twitter, and other social media platforms
- A ready-to-use ML application that can be deployed to solve a real business problem
- Hands-on support from expert AI instructors every step of the way
- A software developer, domain specific business user, or analyst with some programming/scripting background
- You should have familiarity with Python and REST APIs
- No experience creating models necessary
The latest version of Zoom
If you want the recommendation engine to give recommendations based on your items, you should bring a CSV file containing either: user-item interactions, or user-item-rating records.
Recommendation engines learn from previous interactions between users and items. It can include rating of the item (Explicit), reflecting the level of satisfaction from the item or just the interaction that means that the user purchased/clicked/viewed the item (Implicit).
For example:
User Id: 22345 viewed Item Id: 3454
Or
User Id: 22332 gave Item id: 3342 Score: 4If you don’t have data, we will provide sample data for you to use for the workshop
For questions or more information about the required data you can learn more at https://community.cnvrg.io/t/build-your-own-recommender-system-workshop-what-you-need-to-know/53