# Real-Time Anomaly Detection

Enabled low-latency anomaly detection for healthcare operations using production-grade ML pipelines.

## Key Takeaways:

#### Batch-based detection limited operational visibility

Delayed anomaly identification constrained response time and reduced visibility into emerging operational risks.

#### We engineered systems for low-latency detection

System architecture prioritized throughput, reliability, and real-time signal processing to support operational monitoring.

#### High-performance anomaly detection pipelines

Detection services were implemented using Golang for performance-critical components and Python for modeling, deployed on AWS and Databricks to support scale and reliability.

#### Near real-time anomaly detection enabled

Faster detection improved operational responsiveness and confidence across healthcare systems.