At SmartEco, we’ve been exploring an alternative direction to traditional tree-based and gradient-driven classifiers. The result is a geometric, density-aware classification approach designed for environments where latency, memory efficiency, and scalability matter as much as accuracy.
Instead of relying on iterative optimization, deep trees, or large ensembles, this approach maps data into a compact geometric space and performs classification using structured density aggregation. The design intentionally favors deterministic behavior, bounded memory, and predictable performance.
Why This Approach Matters
Modern production systems increasingly face constraints that many mainstream models struggle with:
real-time inference, limited memory budgets, and massive data volumes.
This geometric classifier was built with those constraints as first-class requirements.
Key Characteristics Observed
Microsecond-level inference latency
- Designed for real-time and high-throughput systems where milliseconds are unacceptable.
Non-linear decision capability
- Captures complex patterns beyond linear models, without the overhead of deep ensembles.
Extremely low memory footprint
- Models typically occupy kilobytes to a few megabytes, not hundreds of MBs.
Single-pass training
- Training completes in one deterministic pass over the data... no epochs, no convergence loops.
Scales independently of dataset size
- Once trained, memory usage depends on model configuration... not on the number of training rows
Designed for massive datasets
- Can scale to hundreds of millions or even billions of rows, provided the upstream data pipeline and memory allow it.
Where It Fits Best
This model is particularly suited for:
- Low-latency online inference
- Streaming and real-time decision systems
- Large-scale tabular data
- Environments where memory and predictability are critical
- Applications where training speed and deployment simplicity matter
This work represents an early step in SmartEco’s broader effort to rethink how classical machine learning problems can be addressed under modern production constraints. More details will be shared in future releases.
Alongside this effort, SmartEco is actively developing and maintaining several focused systems, including:
SmartKNN - a low-latency, production-ready k-nearest neighbors model that preserves KNN’s conceptual simplicity while delivering inference speeds suitable for real-time applications.
SmartML - a lightweight benchmarking and evaluation toolkit designed to compare models beyond accuracy, incorporating latency, throughput to reflect real-world ML constraints.
Additional details and open-source releases will be shared soon.
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