ML Engineering / Distributed Computing
Performance Optimization of PySpark AI-Applications
Resource utilization and runtime optimization of PySpark AI-applications
- Industry
- Public Administration
- Client
- Stiftung Zentrale Stelle Verpackungsregister, Osnabrück
- Engagement period
- January 2024 – February 2024
- Delivery mode
- Hybrid
5×Batch runtime reduction
Situation
The client had developed multiple distributed AI applications in PySpark that scaled poorly across their cluster, driven by misconfigurations of Apache Spark and Kubernetes. This limited throughput and inflated compute costs.
Objective
Templefield Technologies was engaged to enhance the existing AI applications by improving runtime efficiency and reducing computational resource demands — without a full rewrite.
Approach
- Tuned Spark distributed-computing configurations for better parallelization and resource utilization
- Refactored application code to remove performance bottlenecks
- Implemented data partitioning strategies to increase processing efficiency
Results
The optimization achieved a 5× reduction in batch run times and a 3× reduction in resource consumption, substantially improving the scalability and operational efficiency of the client's AI systems.