Experience Wallaroo's ML in Production Platform Demo

Schedule Your Wallaroo.AI Demo Today

Talk to an ML Expert

Talk to a Wallaroo.AI machine learning expert today about your unique use case – and schedule your demo.

This demo shows how you can get ML into your production environment faster to impact the bottom line.

Deploy models in seconds
Realize value 4-6X faster
Reduce ML inference cost in production by 50% - 80%
Free up data scientist/ML engineer time by an average of 40%
Real-time drift detection insights to ensure ongoing business value
Run inference up to 12.5X faster
Iterate quickly and scale easily
A self-service toolkit to make your AI team more efficient

Just fill out the form to the right to request your demo!

The Easiest Way to Deploy, Run, and Observe Your ML in Production at Scale

Wallaroo makes it affordable, easy and fast to get AI algorithms live against production data so your production can scale, and you can analyze data up to 12.5X faster while reducing compute costs by 80%.

Deploy

ML models in seconds with a single line of Python code and/or a flexible API.

Run

ML with up to 80% lower cost and easily scale to more data, more models, more complex models.

Observe

Identify sources of model underperformance and production bottlenecks in real-time.

Optimize

Live models based on the latest data and insights with no downtime.

Wallaroo Drives Results: Case Studies

Cybersecurity

A Fortune 100 enterprise needed to deploy over 100 ML models to detect security breaches and the models had to be retrained and updated monthly. Before Wallaroo, the client’s process required 5 data scientists, often taking 2 weeks to deploy a model and needed 600 servers to run them.

Here’s what we achieved:

84% Reduction in computing costs

Data Scientists were freed to focus on innovating

Seconds to deploy updated models

Monetize Your Machine Learning

Wallaroo facilitates the last mile of your ML deployment by removing complexity, automating deployment tasks, reducing infrastructure cost and providing full observability. Now is the time to move your models past the last mile of deployment and impact your bottom line.

Ready to deploy, run and observe your ML in production at scale?