A comprehensive ML monitoring and performance solution is a critical component of successful enterprise production AI. Many MLOps teams consider building in-house monitoring software, which can be costly in terms of time, resources, and financial investment.
In fact, it takes companies an average of 1-2 years or more to build, test, and launch a basic in-house ML monitoring solution. Costs can be significant for cloud computing infrastructure, but enterprise teams also often underestimate the cost and time implications for in-house and contractor labor resources. In many cases, in-house teams run up against cost and time overruns that delay the identification of critical model performance issues and detract from ML innovation.
We've created a user-friendly calculator for you to estimate the potential cost of building an in-house solution vs deploying Arthur, the leading ML monitoring and optimization software solution that delivers insights into model performance across accuracy, data drift, explainability, and fairness.
Accelerate your MLOps GTM launch with a SaaS installation or select Arthur’s on-premise solution which provides the capability to deploy the platform in your data center or virtual private cloud.
Measure model performance with custom and out-of-the-box metrics for tabular, NLP, and CV models.
Keep sensitive data confidential by ensuring proper access control protocols that ensure only users with proper permissions can view the data.
Achieve greater team efficiency. The solution is fully-managed and supported by our engineers 24/7
Ingest models faster with a highly scalable microservices architecture that can ingest up to 1MM transactions per second
Innovate more quickly. Free your in-house team to work on more strategic and visionary projects.
ARTHUR.AI 2022 © All Rights Reserved
ARTHUR.AI 2022 © All Rights Reserved
