Arthur is an MLOps platform that provides monitoring, management, and deployment solutions for machine learning models, including traditional and generative AI. It addresses risks such as data leakage and model performance degradation, enabling enterprises to optimize their AI operations while ensuring compliance and security.
Funding
$63M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises deploying machine learning models, including generative AI, face risks such as data leakage, hallucinations, toxic language generation, and prompt injection. Model performance can degrade over time, leading to inaccurate predictions and adverse business consequences. Ensuring compliance with data privacy regulations and maintaining model security are also significant challenges.
Solution
Arthur is an MLOps platform that provides monitoring, management, and deployment solutions for both traditional and generative AI models. The platform helps organizations deploy AI confidently and safely by protecting against threats like data leakage and hallucinations. It optimizes model operations and performance at scale across various model types, including tabular, computer vision, NLP, and large language models. Arthur's capabilities include model risk management, validation, monitoring, and reporting, enabling enterprises to avoid adverse consequences from model errors and make informed, data-driven decisions.
Target Audience
The primary audience includes enterprises deploying and managing machine learning models, data scientists, MLOps engineers, and AI governance teams.
Features
- Turnkey, plug-and-play solutions for integrating generative AI technologies.
- Protection against LLM threats, including data leakage, hallucinations, toxic language generation, and prompt injection.
- Model-agnostic monitoring platform supporting tabular, computer vision, NLP, and large language models (e.g., OpenAI, scikit-learn, PyTorch, Hugging Face).
- Scalable platform capable of ingesting up to 1 million transactions per second.
- Flexible integrations with data science and MLOps tools, including Databricks, Amazon SageMaker, TensorFlow, PyTorch, SingleStore, and Salesforce.
- Deployment support across SaaS, managed cloud, and on-prem environments.
- Model risk management capabilities across validation, monitoring, and reporting.
- Centralized performance dashboard with real-time metrics, optimization alerts, and customizable permissions.
- SOC 2 Type II compliance, adhering to security and data privacy controls.