~3X faster
~4X cheaper
~7 concurrent tasks
Key challenges
The client struggled to scale Hierarchical Bayesian Regression (HBR) on CPU-based infrastructure. Long runtimes, approximately 12 hours and high costs, limited experimentation and delayed insights. The inability to efficiently process large-scale hierarchical data hindered business agility and model adoption.
Long-running CPU-bound pipelines
Limited experimentation frequency
Poor scalability to large datasets
The solution
Distributed orchestration
Built-in fault tolerance
Spark-managed parallelism
Automatic task distribution
Scalable partition handling
GPU acceleration
Vectorized Bayesian inference
Fast gradient calculations
Parallel model execution
CUDA-based computation
1
Incremental validation
Stage Tests
Scaling
Cost checks
Risk control
2
Low migration
Reused code
GPU flags
Batching
Model Caching
3
Reusable framework
UDF Wrappers
GPU Utilities
Config Templates
Cross-team use
Performance gains
Reduced latency
Faster model training
Parallel execution at scale
Cost efficiency
Lower infra spend
Reduced compute waste
Better resource utilization
Experimentation velocity
Faster iteration cycles
Improved decision speed
More frequent retraining
Scalability
Handles 500K+ entities
Future-ready architecture
Distributed + device-level scaling
