Affine's Anomaly Detection solution allows for the early detection of anomalies in time-series data and integration with various Azure services.
Businesses that rely on machines, equipment, or production lines face the challenge of detecting anomalies in time series data. Failure to detect such anomalies in a timely manner can lead to costly machine downtime, maintenance, and manual interventions. Moreover, reactive measures taken after an anomaly has already caused damage is inefficient and expensive.
Affine's Anomaly Detection solution is designed to monitor time series data and detect abnormalities in real time, allowing customers to identify anomalies in their business or production processes as early as possible. The solution is designed to detect anomalies in multiple variables with correlation, which are typically gathered from equipment, machinery, and production lines.
The solution can be integrated with a wide range of data sources and utilizes various Azure services, including Azure Anomaly Detector, Azure ML, Event Hub, Azure Stream Analytics, Service Bus, Azure Blob Storage, Azure Cosmos DB, Azure Monitor, and Power BI.
This Proof-of-Concept implementation can be expanded to your production environment.
Agenda (9 weeks):
Enabling business-focused data science, AI, and BI development with deep domain expertise.
Affine believes in faster design to faster deployment through key differentiators- Experimentation Focus and Speed to Value.