Anomaly detection is a powerful application of machine learning that identifies outliers in data, or patterns that don’t adhere to expected norms given previous observations. It can be applied in a multitude of business use-cases to drive transformational efficiencies and cost savings, from IT security to preventive maintenance, quality assurance and more. For instance, machine learning to detect anomalies in payment card transaction data is a powerful tool for stopping financial loss due to fraud.
In this live demo you’ll learn:
About new Applied ML Prototypes for Cloudera Machine Learning (CML), which are adaptable sample workflows for enterprise ML applications
How to use CML’s Applied ML Prototype for Anomaly Detection to build and deploy a sample payment card fraud detection model and fraud insights application
Instructions for using the Applied ML Prototype to deliver business value with CDP Public Cloud
