A provider of Wi-Fi-based footfall analytics turned to Toptal to modernize its machine learning capabilities, delivering improved prediction accuracy and a stronger foundation for future model development.
As evolving Wi-Fi signal characteristics reduced the accuracy of an established footfall analytics platform, the client sought to augment its traditional algorithms with machine learning. Limited ground-truth data also made it difficult to validate improvements, leading the engagement to prioritize stronger data collection, experimentation, and evaluation practices alongside model development.
Developed a reproducible machine learning experimentation framework with standardized training pipelines, experiment versioning, and evaluation protocols to support systematic model comparison and reduce overfitting.
Designed a data acquisition and labeling strategy for collecting raw sensor data and ground-truth annotations, enabling supervised model training, benchmark creation, and quantitative algorithm evaluation.
Engineered a fault-tolerant AMQP-based streaming data pipeline with Python and SQL to support continuous ingestion, historical replay, and large-scale model evaluation.
Optimized Python data processing pipelines using just-in-time (JIT) compilation to accelerate feature extraction and historical dataset processing for large-scale model training.
Redesigned the client’s footfall analytics pipeline by replacing rule-based algorithms with machine learning models, improving prediction accuracy and enabling a scalable, data-driven analytics architecture.
Toptal reduced out-of-sample error for a key visit-count algorithm by 2x, accelerated historical data processing by more than 50x, and implemented a production-ready 24/7 AMQP data pipeline.
The team also established Python and SQL tooling, improved ground-truth data collection, and introduced reproducible experimentation practices to support future model development.
