Probabilistic Engine
RxInfer Pro builds on the open-source RxInfer.jl framework. It brings uncertainty-aware modeling to AI workflows and unifies learning, inference, and control under a single probabilistic paradigm.
Implement advanced Bayesian inference with RxInfer. Its elegant API makes complex probabilistic models accessible.
# Instantiate the client from rxinfer import RxInfer client = RxInfer("api-key")# Create a model from a collection of models model = client.models.create_model_instance({ "model_name": "drone-v1", })# Run real-time probabilistic inference result = client.models.run_inference(model.instance_id, { "data": { "observation": get_sensor_data(), } })# Learn from the data client.models.run_learning(model.instance_id, { "parameters": [ "mass", "inertia" ] })
Initialize the client with your API key
Select and configure your model
Run real-time inference
Enable continuous learning
Optimized for performance and scalability
Below is a benchmark comparison between RxInfer's message passing algorithm and Hamiltonian Monte Carlo (HMC) on a linear dynamical system. The benchmark measures time to convergence for inferring the posterior distribution. As shown, on this problem RxInfer's optimized message passing achieves 300x faster inference results compared to traditional HMC sampling.
Optimized message passing with industry-leading performance
Process data with minimal latency for time-critical applications
Optimized CPU and memory utilization reduces operational costs
RxInfer provides a probabilistic layer for AI workflows—capturing uncertainty, supporting streaming updates, and enabling structured reasoning alongside conventional methods.
| Capability | RxInfer | Conventional ML |
|---|---|---|
| Uncertainty Handling | Probabilistic outputs with calibrated confidence | Typically point estimates; uncertainty added separately |
| Adaptation | Supports online and streaming updates | Often retrain-on-batch workflows |
| Model Insight | Structured models with traceable assumptions | Varies by method and tooling |
| Data Use | Effective with limited or noisy data | Generally improves with larger labeled datasets |
Uses probabilistic models to express confidence and robustness in complex environments.
API and abstractions that help teams concentrate on model design and inference logic.
Potential to reduce compute usage depending on workload and modeling choices.
Explore advanced models and applications built with or facilitated by RxInfer, showcasing its versatility.
Stay ahead of evolving conditions with our adaptive learning system. Models continuously update to ensure predictions maintain accuracy as real-world situations change.
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Let AI design your neural networks. Our automated search technology builds optimized models specifically tailored to your unique data patterns and business requirements.
Tackle complex differential equations with confidence. Our Bayesian approach delivers not just solutions, but comprehensive uncertainty quantification for every calculation.
Master dynamic systems with our sophisticated probabilistic framework. Generate real-time insights and predictions for complex, continuously evolving scenarios.
Dive deeper into the capabilities of RxInfer and see how it can revolutionize your data-driven applications.
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