Forbes debuted its AI 50 list , with Arize AI again listed among the privately-held North American companies making the most interesting and effective use of artificial intelligence technology.
“We are incredibly proud to make the Forbes AI 50 list for the second consecutive year,” says Jason Lopatecki, CEO and co-founder of Arize. “As category leaders start to break out in this space, Arize is uniquely positioned in ML observability to help any organization troubleshoot complex AI systems and resolve issues faster.”
To winnow down the hundreds of organizations eligible, Forbes selected finalists based on metrics such as revenue gains, customer statistics, historical funding and valuation. A panel of expert AI judges then evaluated more than 100 finalists to find the 50 most compelling companies.
Arize is the only machine learning observability platform to make the list this year. Other companies listed include 6sense, Anyscale, Databricks, Generate Biomedicines, Hugging Face, and others.
Today’s recognition comes on the heels of a slew of recent product innovations from Arize. Most recently, the company debuted Arize Bias Tracing, a tool designed to help monitor and take action on model fairness metrics. Arize also democratized access to its machine learning observability platform earlier this year, offering a free tier to ensure that every organization can detect, root cause, and resolve model performance issues faster.
Arize was founded in 2019. An early pioneer and leader in machine learning observability and model monitoring, Arize AI already tracks hundreds of billions of predictions a month on behalf of large enterprises and disruptive startups
Arize AI is a machine learning observability platform that helps ML practitioners successfully take models from research to production with ease. Arize’s automated model monitoring and analytics platform help ML teams quickly detect issues when they emerge, troubleshoot why they happened, and improve overall model performance. By connecting offline training and validation datasets to online production data in a central inference store, ML teams can streamline model validation, drift detection, data quality checks, and model performance management