Graph Neural Networks Course
Dive into Graph Neural Networks to enhance microservice monitoring and predict failures effectively. This course covers graph construction, GNN model architectures like GraphSAGE and GAT, feature engineering techniques, robust training methods, evaluation strategies, and seamless production deployment to create scalable, dependable systems in cloud infrastructures. You'll gain hands-on skills to apply GNNs confidently in real-world reliability challenges.
from 4 to 360h flexible workload
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. You'll cover graph basics, key observability metrics, and feature engineering for accurate incident forecasting, then build GraphSAGE and GAT models with streamlined data pipelines and training processes. Lastly, deploy, track, and refine GNN models in live production settings for reliable, quantifiable outcomes.
Elevify advantages
Develop skills
- Design microservice graphs by mapping dependencies, metrics, and failure indicators quickly.
- Build GNN models using GraphSAGE and GAT for predicting service health accurately.
- Engineer graph features by creating node and edge signals for outages and SLA risks.
- Train and evaluate GNNs with strong training loops, baselines, and metrics that detect drift.
- Deploy GNNs in production environments by serving, monitoring, and scaling low-latency models.
Suggested summary
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