This is the third Applied Machine Learning Prototypes. (Here are the links for the Customer Churn and Anomaly Detection prototypes). These are prototypes that will help you build a fully working machine learning example in CML. The Templates will include source data, and walk through various steps:
Ingest data into a useful place in CDP (e.g. a Hive Table)
Explore the data set
Create a plan to build a model
Train the model
Deploy the model
Build and deploy an application
Once you have deployed the template and all the CML artifacts that go with it, you can unpick and work it backward to map the process to your own data in your own environment.
Our latest Prototype - Fraud Detection - is now available. To get up and running with it, do the following:
Log in to your CML workspace and create a project using the following repo:
This is the URL to the Git section in the Initial Setup:
This will deploy the files into your CML instance and will look like the following:
From here, follow the instructions in the README. It will take through all the steps required to build a Sentiment Analysis application using Shiny that lets you interact with two different sentiment prediction models.