Wave Autoscale Intelligence

Description

Wave Intelligence is the machine learning engine powering Wave Autoscale's predictive autoscaling and performance optimization capabilities. Built with Python and FastAPI, it delivers real-time forecasting, workload prediction, and intelligent scaling recommendations.

Key Features:
- Time-series forecasting for predictive autoscaling using statsforecast models
- Performance prediction models for resource optimization
- Horizontal scaling algorithms with cost and performance strategies
- Cluster resource capacity forecasting (7-30 days ahead)
- Memory leak detection and anomaly identification
- Workload-specific model training using historical usage patterns
- Real-time metrics analysis with DuckDB and Polars
- RESTful API integration with Wave Autoscale Core

ML Capabilities:
- Forecasting-based predictive scaling for proactive resource allocation
- Min/Max replica recommendations based on workload analysis
- Autopilot scheduler with cron-based configuration for business events
- CPU utilization breakdown and performance bottleneck analysis
- Smart sizing recommendations for container resource rightsizing

Technology Stack: Python, FastAPI, Polars, NumPy, statsforecast, DuckDB, uvicorn/gunicorn

The service runs on port 3026 and integrates seamlessly with Wave Autoscale Core to enable ML-driven Kubernetes optimization.