As we continue building smarter, self-optimizing optical networks within the ALLEGRO platform, the role of AI and Machine Learning becomes increasingly central. Our AI Engine is designed to handle the entire lifecycle of ML models—from training to deployment, inference, and performance monitoring.
🚀 What makes it powerful?
🧠End-to-End ML Lifecycle Management
- Supports time-series ML for short-term link performance prediction
- Enables end-to-end AI applications for network optimization
- Integrated with time-series databases for real-time training & inference
- Automated data sampling, pre-processing, and aggregation across distributed databases
🔧 Built with OpenFaaS on Kubernetes
- Serverless, containerized deployment of ML models
- Language-agnostic—ideal for diverse AI projects
- Scalable and resilient, supporting rapid iteration and high availability
- Seamless integration into the ALLEGRO cloud-native ecosystem
đź“Š Real-Time Monitoring with Prometheus
- Tracks performance and resource usage of deployed ML functions
- Ensures operational transparency and efficiency
This AI Engine gives network operators the ability to make real-time decisions, detect faults early, and dynamically optimize network performance—making AI not just a feature, but a core part of the network’s intelligence.
💡 Empowering the future of autonomous, high-performance optical networks—one ML model at a time.
#ALLEGROProject #AIinNetworking #OpticalNetworks #MachineLearning #MLOps #OpenFaaS #Kubernetes #Prometheus #EdgeAI #NetworkOptimization #Telemetry #CloudNative #TimeSeriesData #SmartNetworks #TelecomInnovation