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.
Request a demoThe fastest way to scale AI to ROI, right from the comfort of your own data science home
Start inferencing at scale in seconds with the Wallaroo platform, using the infrastructure you’re already familiar with
Monitor, test, and manage your production ML in Wallaroo’s unified operations center, shortening feedback loops and gaining agility
Get dedicated experts who’ve already been there and done that to support you as needed, from deployment to scaling to optimization
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.
Sources
prep data
ML models
ML Platform
Outcomes
From Python Notebook to real-world results, much faster.
(while you free up your data scientists’ and engineers’ time)
Easy-to-use SDK, UI, and API for fast, repeatable, low-code/no-code ML operations
Distributed computing core written in Rust-Lang uses up to 80% less infrastructure
Comprehensive audit logs, advanced model insights, full A/B testing
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
- 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
ML Environments
Operations Center
On Prem
Server
On Prem
Server
On Prem
Server
Locations
Server
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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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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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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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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