Anomaly Detection KMeans

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Presentation transcript:

Lumada Analytics Example Machine Learning Solutions Anomaly Detection KMeans Solution

Anomaly Detection KMeans Anomaly Detection using KMeans Model Training using KMeans methods Cluster Assignment to determine anomalies data Scaling R Stages to apply models

Anomaly Detection KMeans Composition

Model Training Workflow KMeans Training Workflow Batching Data Pipeline Python Training Model Training Data Models f(x) Data Lake (Solr) File Storage Service

Applying Model Workflow Cluster Assignment Workflow Streaming Data Pipeline CONNECTOR AMQP R Apply Model AMQP Output Stage RabbitMQ Exchange f(x) E Results Solr Models File Storage Service