Wallaroo.AI

Founded 2019

Deploy, manage, and monitor machine learning models in production environments

You can use Wallaroo to deploy, serve, and monitor machine learning models at scale. It lets data science teams upload models written in Python, PyTorch, TensorFlow, or ONNX directly into production with minimal configuration. The system delivers high-throughput inference while consuming fewer computing resources across cloud, on-premises, and edge hardware. You can track model performance in real time, identify data drift, and set up automated alerts when accuracy drops. It simplifies testing new model versions and auditing model behaviors over time.

What Wallaroo.AI does

Upload trained models to a centralized model registryDeploy inference pipelines to Kubernetes clustersSet alert thresholds for feature drift and prediction driftConfigure canary deployments for live model testingExport operational logs to external monitoring stacksPackage models into lightweight containers for edge hardwareUltra-low latency inference engine designed for high throughputOne-line deployment of Python, PyTorch, and TensorFlow modelsAutomated drift detection and performance monitoringEdge deployment support for constrained hardware environmentsA/B testing and shadow deployment capabilitiesDetailed audit logs for enterprise governance and compliance