Learn BIGDL with Real Code Examples

Distributed training scales linearly with cluster nodes for large datasets

CPU performance optimized via Intel MKL

GPU acceleration available for high throughput

RDD caching improves iterative training performance

Streaming inference may require careful memory management

Secure sensitive datasets with HDFS or cloud permissions

Restrict access to Spark clusters

Audit distributed model training logs

Validate input data to prevent model poisoning

Follow enterprise data governance policies

Track training loss and accuracy metrics

Visualize distributed job performance via Spark UI

Log inference throughput and latency

Compare multiple model runs

Audit predictions for consistency

Document model layers and parameters

Maintain reproducible Spark jobs

Use versioned scripts for distributed training

Test models on sample and full datasets

Monitor training logs for consistency