Learn BIGDL with Real Code Examples

Train CNN for image classification on ImageNet dataset using Spark cluster

Train RNN for text prediction using distributed data streams

Build recommendation system with user-item interactions at scale

Evaluate model using distributed metrics and logging

Deploy trained model for batch or streaming inference on Spark

Ensure Spark cluster memory and cores are properly configured

Monitor distributed training logs for performance bottlenecks

Check data partitioning for balanced workload

Validate model serialization and deserialization

Update BigDL and Spark versions for compatibility

Train on sample dataset before scaling

Validate training convergence with metrics

Test batch vs distributed execution

Check model serialization and loading

Verify inference correctness on cluster nodes

Deploy trained models on Spark cluster for batch inference

Use Spark Structured Streaming for real-time predictions

Export models to ONNX/TensorFlow for serving elsewhere

Integrate BigDL with production ML pipelines

Automate retraining pipelines with Spark jobs

Apache Spark 3.x for distributed computation

Hadoop/HDFS or cloud storage for big data

Python/Scala APIs for model scripting

TensorFlow/PyTorch import/export for interoperability

MLlib for complementary ML tasks

Spark SQL and DataFrames for preprocessing

Streaming pipelines via Spark Structured Streaming

ONNX and TensorFlow/Keras models import/export

Cloud object storage (S3, Azure, GCS)

BigDL + Spark MLlib hybrid pipelines

Cache data to improve training speed

Use small-scale experiments before full cluster training

Keep pipelines modular

Leverage existing Spark ML and SQL for preprocessing

Monitor cluster resources to prevent bottlenecks

Optimizing cluster resources for training

Debugging distributed models

Handling data skew and partitioning

Ensuring reproducibility across nodes

Integrating with heterogeneous big data sources