Wallaroo.AI

Turnkey Optimized AI inference for any model, any hardware, anywhere.

Freemium

Description

Wallaroo.AI provides a comprehensive platform designed to streamline the deployment, management, and observation of machine learning (ML), artificial intelligence (AI), and generative AI models in production environments. It caters to AI teams facing pressure to operationalize models quickly and efficiently, addressing common bottlenecks associated with MLOps and LLMOps. The platform facilitates standing up ultrafast, turnkey inference microservices on CPUs or GPUs, across any cloud, on-premise, or edge location without extensive engineering effort. Users can manage and automate their entire production AI workflow from a single, centralized interface.

The core benefits of using Wallaroo.AI include significantly faster time-to-value, with claims of achieving AI production up to six times faster through automation and self-service tools. It enables organizations to support substantially more AI deployments while reducing associated costs by up to 80% via efficient resource utilization, automated scaling, and a high-performance Rust-based inference engine. The platform emphasizes seamless integration with existing data science tools and infrastructure, offering advanced observability features like drift detection, A/B testing, and real-time monitoring to ensure optimal model performance and continuous improvement without downtime.

Key Features

Self-Service Toolkit: Easy-to-use SDK, UI, and API for rapid, repeatable deployment operations.
High-Performance Inference Server: Distributed Rust-based compute engine supporting x86, ARM, CPU, and GPUs for fast inference.
Advanced Observability: Provides comprehensive audit logs, model performance insights, and A/B/shadow testing capabilities.
Flexible Integration: Seamlessly connects with existing ML toolchains like notebooks, model registries, and experiment tracking tools.
Cost Reduction: Claims up to 80% reduction in deployment costs through efficient resource use and a fast Rust-based server.
Scalability: Supports workload autoscaling and efficient handling of large data volumes for both real-time and batch inferencing.
Centralized Management: Offers a unified UI for collaborative model management, monitoring, and optimization with access control.
Integrated Model Validation: Features built-in A/B testing, canary deployments, and automated drift detection.
Edge & Multi-Cloud Support: Simplifies deployment and management of AI models across various edge devices and cloud environments.
GenAI Deployment: Streamlines deployment and scaling of generative AI models with low-code options and support for popular frameworks.

Use Cases

Accelerating AI model deployment to production
Managing and observing ML models at scale
Optimizing inference performance and cost
Deploying computer vision models in constrained environments
Implementing forecasting and classification models
Operationalizing Generative AI and Large Language Models (LLMs)
Scaling AI applications in Retail and MarTech
Deploying AI for FinTech and Financial Services
Utilizing AI in Oil & Gas and Manufacturing
Implementing AI solutions in Life Sciences & Healthcare
Running AI workloads in Aerospace & Defense

Frequently Asked Questions

What does the Wallaroo.AI platform do?

Wallaroo.AI allows you to deploy, serve, observe, and optimize AI models in production using automation. It helps manage any model across various target environments (cloud, edge, on-prem) within your security framework, reducing engineering time, delays, and inference costs while providing high-performance inferencing.

What advantages does Wallaroo.AI provide?

Wallaroo.AI offers a fast way to operationalize AI at scale, providing efficiency, flexibility, and ease of use across cloud, multi-cloud, and edge environments.

What deployment targets do you support?

The platform supports deployment to on-premise clusters, edge locations, and cloud-based machines in AWS, Azure, and GCP.

How long does it take to learn to deploy a model in Wallaroo.AI?

It typically takes minutes. Most models can be deployed using just three lines of Python code. Wallaroo.AI also provides customized training sessions.

What languages or frameworks does the Wallaroo.AI platform support for deployment?

It supports low-code deployment for Python-based or MLFlow-containerized models, and offers lighter-weight deployment for common frameworks like Scikit-Learn, XGBoost, Tensorflow, PyTorch, ONNX, and HuggingFace.