One Platform for Full Production Machine Learning
A unified production ML platform for any deployment environment
Going from ML prototype to an actual AI-based product that provides business value is too often thought of as just wrapping an ML model in a container and deploying it on to a server.
In reality, it’s never that easy.
Wallaroo.AI focuses on the full life cycle of production ML – not just deployment but also observability, validation, and optimization.
We empower enterprise AI teams to deploy, observe, test, and optimize ML from the cloud to the edge with efficiency, scale, and ease.
Find out the fastest way to get AI to ROI – fill out the form to the right for a consultation with the ML experts at Wallaroo.AI.
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Easy
Quickly deploy and manage models with simple commands with little or even no ML engineering needed.
- Simple SDK and UI for Data Scientists
- Works/integrates with our ecosystem toolkit
- Deploy anywhere (any cloud, in distributed networks, at the edge) with minimal fuss
- Integrated ML monitoring and management to drive value
Scalable
Scale to many models, many endpoints, and many use cases, and/or lots of data.
- Simplify management of complex models and ML pipelines
- Scale with lower cost and overhead
- Repeatable processes no matter where or how your models are trained
Efficient
Work within existing ML workflows and data ecosystems without requiring major migrations or limiting what frameworks and tools data scientists can use
- 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
Production ML made easy, scalable, and efficient
Deploy and serve anywhere at scale
Centralized monitoring and continuous optimization
Flexible integration with your ecosystem, your tools, your infrastructure
1 Platform. 3 Core Components. Full Production ML Capabilities.
In order to truly operationalize ML in production, AI teams need to consider the full model operations lifecycle beyond deployment and think of production ML in terms of continuous iteration, continuous deployment.
Model Operations Center
Deploy & manage models in seconds with full governance & observability
Inference Server
Generate more inferences, faster, on less compute in any cloud, on prem or at the edge
Integration Toolkit
Integrate into your ecosystem so your data scientists can keep using their favorite tools
Wallaroo.AI meets you where you are using the tools you use.
In addition to our rich integration toolkit to the most common data sources and sinks, Wallaroo.AI works closely with key partners and communities to create better experiences.
By working closely with Wallaroo.AI, our customers will be able to run and manage machine learning at scale across cloud, RAN, and edge environments that together will make up the low latency, highly flexible networks of the future.
Wallaroo.AI’s efficient inferencing at the edge coupled with its global model observability and management capability is a great strategic fit with OGA’s effort to scale and optimize distributed and edge ML operations across use cases and industries.
Enable collaboration and agility while enhancing security and control.
The conventional approach to operationalizing machine learning involves data scientists building a model and then handing that model over to ML engineers to deploy into production. The problem with this waterfall approach is ML models do not lend themselves to clean handoffs from data scientist to engineer. ML models require constant monitoring and tweaking even in production as the world continually changes.
We’ve built Wallaroo.AI on a workspace-based paradigm to make it easy for administrators to give the right kind of access to the different user types.