All Production Patterns

15 guides for deploying PyTorch Geometric in enterprise environments. What breaks in production, how to fix it, and how Kumo handles it automatically.

Building Heterogeneous Graphs from Relational Databases

Convert tables and foreign keys into PyG HeteroData objects.

Neighbor Sampling Strategies for Large Graphs

NeighborLoader, fanout tuning, and temporal sampling.

Handling Time in Graph Neural Networks

Temporal encoding, leakage prevention, and dynamic graphs.

Mini-Batching on Graphs: Why It's Different

Batch vectors, graph merging, and variable-size batch handling.

Scaling PyG to Billion-Node Graphs

Partitioning, distributed storage, and feature compression.

Speeding Up PyG with torch.compile

30-35% training speedups with compilation pitfalls.

Serving GNN Predictions in Production

Batch and real-time inference with embedding caches.

Connecting PyG to Feature Stores

Feast, Tecton, Redis, and PyG's RemoteBackend.

PyG + Snowflake: Graph ML on Your Data Warehouse

ETL pipeline from Snowflake to PyG with temporal correctness.

PyG + Databricks: Graph ML on the Lakehouse

Spark preprocessing, Delta Lake, and Unity Catalog.

Handling Cold-Start Nodes in Production

Inductive learning, feature fallback, and edge dropout.

Explaining GNN Predictions for Compliance

GNNExplainer, attention analysis, and regulatory requirements.

Multi-GPU and Distributed GNN Training

DDP, sampling overhead, and gradient synchronization.

Choosing the Right GNN Architecture

Decision framework based on graph type, task, and constraints.

GNN Evaluation: AUROC, MAP@k, and Beyond

Metrics, temporal splits, and segmented evaluation.

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