Artificial Intelligence (AI) has become an increasingly prevalent part of our daily lives, but ensuring these models are accurate and reliable remains a complex task. Traditional AI evaluation methods can be time-consuming, requiring substantial manual setup, with no specific framework or guidelines for working on models. This has led to engineers having to manually inspect faulty rows before deployment to understand and enhance models.
Openlayer aims to change this by providing a simple, automated approach to AI evaluation. Openlayer is a tool that integrates seamlessly into your development and production pipelines. It helps developers create high-quality models with confidence by establishing a series of ‘must-pass’ tests for their AI system, connected to their Github repository. Developers can either create their own tests or choose from a pre-existing library of tests. The integration triggers these tests with each code commit, ensuring a steady appraisal of the model without the developer needing to invest additional time.
Openlayer’s solution simplifies the assessment process, allowing developers to identify and rectify bugs more often. As a result, the overall quality and reliability of AI models are enhanced, freeing up valuable time for developers to work on creative projects. In addition to automatic testing and real-time monitoring, Openlayer enables developers to track, test, and deploy models. It also offers SOC 2 compliant on-premise hosting and aids collaboration by allowing users to monitor their progress.
Recently, Openlayer raised $4.8 million in a seed round led by Quiet Capital, with additional funding coming from Ground Up VC, Y Combinator, Picus Capital, Hack VC, Liquid2 Ventures, Soma Capital, and Mantis VC.
In conclusion, Openlayer provides a beneficial tool for simplifying the often challenging process of AI evaluation. Its focus on automating testing, real-time monitoring, and effortless integration enables the development of more reliable Machine Learning models. Moreover, Openlayer’s focus on improving the developer experience and its commitment to security, make it instrumental for advancing AI development.
