See the webinar for this Prototype here: https://www.cloudera.com/about/events/webinars/building-diverse-anomaly-detection-capabilities-for-b...
We now have our second Applied Machine Learning Prototypes available (Go here for the previous Prototype - Customer Churn). These are prototypes (or templates as they were called) 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:
https://github.com/fastforwardlabs/cml_fraud_demo
- 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. If you just want to deploy the whole project and get the application up and running quickly, launch a new Workbench session:
- Once the Workbench is open, open file 6_build_project.py and run the file:
- When the script completes the run, your project will look like the following:
- Launch the application from the Applications tab and click on the blue arrow next to the name:
- This will open the application in a new window. This app is a Dash app that shows some sample data and the prediction that the model made.
