“Michaelangelo is Uber’s end-to-end ML platform that powers 100% business-critical ML use cases at Uber to deliver a consistent user experience across billions of rides and deliveries. Given the vital role ML plays in this process, it’s critical to have tools that build on Michalangelo’s core capabilities and help us stay ahead of potential production ML problems. We’re excited to work with Arize AI to enhance platform ML observability capabilities and make it easier to detect and resolve model performance issues.”

Kai Wang

Product Lead - Uber AI Platform

"We believe that products like Arize are raising the bar for the industry in terms of ML observability. Standardized model observability will soon become a best practice and an integral part of any new machine learning projects in production, similar to what Datadog and Grafana have achieved in software engineering. We are excited to be part of this evolution of data science, as we continue working with Arize and leverage the platform’s capabilities."

Mihail Douhaniaris and Steven Mi

Data Scientist and Senior MLOps Engineer, respectively

"Earlier this year, Wayfair chose Arize as its model monitoring solution...Since model monitoring is a vital component of MLOps, Wayfair’s ML platform team is working closely with Arize AI to develop a long term strategy around onboarding."

Duncan McKinnon

Machine Learning Engineer 2

“The ability to quickly change what we’ve built, understand how it’s different from the previous models and know where it has problems is mission-critical … to our commitment to innovation and leadership in the increasingly privacy-focused advertising environment.”

Alok Kothari

Director of Machine Learning, Adobe

"The team evaluated several options and ultimately chose Arize due to its strong support, effective onboarding process, and commitment to helping us scale up our skills to consistently leverage the tool. Arize will help us further improve our models’ performance, ensure the quality of our data, and maintain fairness and transparency in our machine learning processes."

Valeria Gomes

Data Lead

"We recently deployed a model that went from inception to production in 46 days – hardly a small endeavor given the model is relied on to score over 50,000 insurance applications daily. Arize is a big part of that success because we can spend our time building and deploying models instead of worrying – at the end of the day, we know that we are going to have confidence when the model goes live and that we can quickly address any issues that may arise."

Alex Post

Lead Machine Learning Engineer

"Machine learning is a discipline where few notice when everything is performing perfectly — and everyone notices when things go wrong. In that sense, it’s not a question of whether you need ML observability — you do — it’s more a matter of whether to build or buy. For us, Arize was the clear choice in terms of cost efficiency and freeing us up to achieve our broader vision."

Richard Woolston

Data Science Manager

“Arize was really the first in-market putting the emphasis firmly on ML observability, and I think why I connect so much to Arize’s mission is that for me observability is the cornerstone of operational excellence in general and it drives accountability.”

Wendy Foster

Director of Engineering and Data Science, Shopify

“Some of the tooling — including Arize — is really starting to mature in helping to deploy models and have confidence that they are doing what they should be doing.”

Anthony Goldbloom

Co-Founder & CEO, Kaggle

"Models are never perfect; they are always going to drift based on changing behaviors, changing data, or changing source systems. Having a centralized monitoring platform like Arize is immensely beneficial."

Malav Shah

Data Scientist II

"Arize really values understanding and helping the data scientist – a platform built by and for tech people – which resonates with our team."

Habib Baluwala

Domain Chapter Lead for Commercial Data

“The Arize AI platform provides an intuitive UI that’s easy to use and can monitor drift and performance of all models across our most advanced communication deployments.”

Brendon Villalobos

Machine Learning Technical Lead, Twilio

“It is critical to be proactive in monitoring fairness metrics of machine learning models to ensure safety and inclusion. We look forward to testing Arize’s Bias Tracing in those efforts.”

Christine Swisher

VP of Data Science, Project Ronin

“As an organization, we generally build rather than buy – particularly for our AI and machine learning infrastructure. So it’s a high burden to meet, and Arize meets it in terms of helping sophisticated organizations like Shelf Engine that don’t do off-the-shelf data science.”

Stefan Kalb

CEO, Shelf Engine