AI Startup Arize AI Secures $19 mln in Series A Financing

Arize AI, an AI ML startup based in California, has raised $19 million in Series A Financing to help developers move their machine learning models into production.

The funding round was led by Battery Ventures with participation from existing investors like Foundation Capital, Trinity Ventures, the House Fund, and Swift Ventures. This capital brings the total funding raised by the startup to $23 million. Dharmesh Thakker, General Partner at Battery Venture who will be joining Arize’s Ai board of directors, said about the potential the firm saw in the startup:

“As the world becomes increasingly AI-centric, there will be a few primary categories of ML infrastructure tools that truly matter for data organizations. Billions have been invested in two categories, data preparation and ML model building; leading to a flood of models being deployed across every industry. However, the actual value of a model’s impact on business and customers is often hazy at best. Similar to how solutions help teams manage their software infrastructure investments, organizations that are serious about ML need to employ a toolchain for ML infrastructure.”

Arize AI was founded to revolutionize how machine learning is being used as the backbone of modern technology by facilitating the process of translating models from the research to the production stage. Historically, developers have found that issues that went undetected during the research stage cause issues and disruptions when in the production stage.

The startup has developed a platform that provides full-stack machine learning observability and model monitoring, increasing transparency and accountability while reducing disruptions. Aparna Dhinakaran, co-founder and chief product officer at Arize AI, referred to the increasing demand for such solutions by stating:

“In the same way that tools had to be created in the software industry to track issues, manage version history, oversee builds, and provide monitoring, we’re seeing a similar trajectory in the ML space. Without the tools to reason about mistakes a model is making in the wild, teams are investing a massive amount of money in the data science laboratory but essentially flying blind in the real world.”

Machine Learning and AI are being applied to several industries at a fast pace due to the benefits they provide in terms of optimization and efficiency. By creating a platform that facilitates the deployment of these models, the AI startup expects to boost the adoption of the technology while also providing all parties involved with a better experience.