90% of AI initiatives fail to produce ROI. We solve that.

Deploy, scale, monitor, and optimize production ML from the cloud to the edge quickly, efficiently, and using minimal engineering with our ML ops production platform.

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The fastest way to scale AI to ROI, right from the comfort of your own data science home

Deploy + Serve

Start inferencing at scale in seconds with the Wallaroo platform, using the infrastructure you’re already familiar with

Observe + Optimize

Monitor, test, and manage your production ML in Wallaroo’s unified operations center, shortening feedback loops and gaining agility

Expert ML Team

Get dedicated experts who’ve already been there and done that to support you as needed, from deployment to scaling to optimization

How it works

The Wallaroo Enterprise Platform for ML in Production. Your Tools, Your Data, Your Ecosystem.

Wallaroo.AI provides a simple, secure, and scalable production capability that fits into your end-to-end workflow and runs where you need it to.

Load &
prep data
ML models
Wallaroo Production
ML Platform
Data Lake

From Python Notebook to real-world results, much faster.

(while you free up your data scientists’ and engineers’ time)

Deploy in seconds Self-Service toolkit to deploy and scale ML

Easy-to-use SDK, UI, and API for fast, repeatable, low-code/no-code ML operations

Scalability + performance Blazingly Fast Inference Server

Distributed computing core written in Rust-Lang uses up to 80% less infrastructure

Continuous Optimization Advanced Observability

Comprehensive audit logs, advanced model insights, full A/B testing

What makes Wallaroo.AI different?

Breakthrough speed and agility in the cloud or at the edge

Flexible integration with your tools and infrastructure

  • Integrates with your ML toolchain (notebooks, model registries, experiment tracking, etc.)
  • Intuitive APIs and Connectors for custom workflow integrations
  • Works with specialized ML acceleration libraries

Ultrafast Rust-based Server that lowers cost by 50% – 80%

  • 3X- 13X faster Batch Inferencing
  • Real-time Inferencing Latency as low as 1 microsecond
  • 5X more efficient data handling
  • Workload autoscaling

(As benchmarked with the ALOHA open-source model, various TF, XGBoost, and Computer Vision models in AWS, Azure, GCP, and Databricks)

Centralized model management, observability, and optimization

  • Collaborative Workspaces with access control
  • Automated feedback loop for ML monitoring and redeployment
  • Integrated Model Validation with A/B testing and Canary deployments
  • Easy-to-use, unified UI
Learn more about the Wallaroo.AI platform
  • Simple SDK and UI for Data Scientists
  • Works/integrates with customer’s toolkit
  • Deploy anywhere with minimal fuss
  • ML Monitoring and management to drive value
  • Many models, many endpoints, many use cases
  • Lots of data
  • Complex models and ML pipelines
  • Scale with lower cost and overhead
  • Lower resource requirements for ML Workloads
  • Quick resolution of ML model issues
  • Let the people you already have get more done
  • Fast time-to-value

Turbocharge your ML workloads in your own cloud, your customer’s remote cloud, or at the edge

Your Data Science /
ML Environments
Operations Center
Your Cloud /
On Prem
Your Cloud /
On Prem
Remote Cloud /
On Prem
Fortune 100

A Fortune 100 enterprise needed to deploy over 100 ML models to detect security breaches. Plus, the models had to be retrained and updated monthly.

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A medical device manufacturer was looking for a simple and scalable way to deploy and manage models to thousands of endpoint devices.

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Financial Services

One of the top 10 banks in the world needed to analyze billions of streaming events in real time while making it easy and fast to update cybersecurity models.

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Food Service

An online food delivery firm wanted to know instantly when their fraud detection models started to drift.

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A large software company wanted to focus their sales team on the highest propensity prospects by generating better insights from calls.

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The Detroit Lions sought to maximize revenue for live event spot ticket prices during the course of the National Football League (NFL) season.

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The US Military needed to analyze petabytes of daily IoT data in the cloud and across millions of edge devices, including drones and ships, to swiftly detect security anomalies.

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Real Estate

One of the top 5 real estate investment trusts (REITs) in the U.S. needed to scale dynamic pricing to hundreds of regions and thousands of locations.

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An analytics and management software firm wanted to optimize their own customers’ complex buying journeys, integrating both first-party and third-party data into their analysis.

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