moco automatically converts existing classifiers into routed cascaded systems that avoid unnecessary computation while preserving model behavior.
Most production ML systems send every input through the full model — even when many predictions are trivial to classify.
moco analyzes model behavior and automatically routes easy predictions through cheaper decision paths while reserving full inference for difficult cases.
Reduce unnecessary inference compute.
Reduce energy consumed per prediction.
Process more requests on existing hardware.
Not sure whether your ML system is a fit?
I will analyze historical inference logs, model outputs, or representative datasets to identify unnecessary model computation and estimate potential savings.
The audit evaluates:
Ideal for fraud detection, NLP classification, cybersecurity, edge AI, and large batch inference workloads.
Book a Free AuditEdge Systems: Extend battery life, reduce thermal usage of GPUs.
Large Batched Workloads: Reduce compute costs.
Real-Time Systems: Increase headroom for the more complex analysis of the inputs that need it.
moco exposes a variety of API endpoints that optimize ML models.
These endpoints return routed systems that output the same predictions as the original model.
Teams can "plug-and-play" these routed systems into their pipelines.
A preview of the API docs is here.
The product is available for alpha testing.
Python Example of the Python pip installable package. Inquire if you would prefer this.
import moco
rules = moco.analyze(dataset: np.ndarray, predictions: np.ndarray)
selected_rules = [rule for rule in rules if rule.class in (0, 1, 2)]
optimized_model = moco.build_cascaded_system(original_model, selected_rules)
moco is a post-training optimization tool that improves classification efficiency. Given a calibration dataset and a chosen representation of the data, moco identifies low-complexity rules that simplify the decision problem itself by decomposing the decision into easier-to-solve subproblems.
Can be applied in combination with quantization/pruned models. Quantization & pruning alike risk accuracy degradation.
Knowledge Distillation occurs at training time, and requires significant development cost to optimize the model for deployment, meeting both latency & accuracy targets.
Cascaded systems are great! Why evaluate a complex ML model if a simple rule will suffice? moco scales this idea and automatically generates these rules, and builds a routed system automatically.
| Task | Dataset | Base Model | Fine Tuned Model | Compute Reduction | Accuracy Change |
|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | ResNet-18 | HF Model finetuned on CIFAR-10 | 34.6% | -0.3% |
| Text Classification | IMDB Reviews | BERT | Fine-Tuned TinyBERT | 21.5% | +0.1% |
Evaluation Steps