Graph Neural Networks Course
This course equips you with skills to master Graph Neural Networks for microservice monitoring and failure prediction. Explore graph modelling, key GNN architectures like GraphSAGE and GAT, feature engineering, robust training techniques, and seamless production deployment to create scalable, dependable systems in cloud setups. Ideal for enhancing system reliability with data-driven insights.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain expertise in graph neural networks for predicting service failures and boosting reliability through this practical course. Cover graph basics, observability metrics, and feature engineering for accurate incident forecasting, implement GraphSAGE and GAT with optimised data pipelines and training, and deploy, monitor, and refine GNN models in production for tangible outcomes.
Elevify advantages
Develop skills
- Design microservice graphs by mapping dependencies, metrics, and failure indicators swiftly.
- Build GNN models using GraphSAGE and GAT for predicting service health effectively.
- Engineer graph features by creating node and edge signals to detect outages and SLA risks.
- Train and evaluate GNNs with strong training loops, baselines, and metrics that account for drift.
- Deploy GNNs in production environments by serving, monitoring, and scaling low-latency models.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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