The fastest way to operationalize AI at scale.
Deliver real-world results with incredible efficiency, flexibility and ease in any cloud, multi-cloud and at the edge.
Request a demo90% of AI initiatives fail to produce ROI.
Over 100 Chief Data Officers report these key challenges:
We help solve that.
From your notebook to real-world results with minimal engineering or delays
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
Wallaroo.AI Enterprise Platform for Production AI at Scale Your Data, Your Ecosystem. Our Platform. Our Team.
Designed for Data Scientists and ML Engineers alike
Sources
prep data
ML models
ML Platform
Outcomes
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
Go from Python Notebook to Results, Fast
Easily scale number of live models by 10X with minimal effort and via automation
- Focus on business outcomes
- Deploy models in seconds via automation and self-service
- Robust API and data connectors means easy integration
Up to 12X faster inferencing, 80% lower cost, and free up 40% of AI team’s time
- Target x86/ARM/CPU/GPU via simple config
- Don’t be blocked on GPU availability
- Sub-millisecond latency + efficient analysis of large batches
Continuously observe, optimize AI in production to get to value 3X faster
- Troubleshoot live models in real-time
- Retrain and hot-swap live models
- Centrally observe and manage local or remote pipelines
Turbocharge your AI workloads in your own local cloud, a 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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