Arize AI Selected For insideBIGDATA’s Impact 50 List

Krystal Kirkland

Application Engineer

BERKELEY, Calif.Oct. 28, 2020 /PRNewswire/ — Arize AI, We’re excited to announce that Arize AI is selected for insideBigData’s Impact 50 list in Q4 2020. Companies on the list exhibit technology leadership, strength of offering, proven innovation, positivity of message, quality perception in the enterprise, intensity and frequency of social media buzz, high profile of members of the C-suite, and so much more!

The team at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

“We are thrilled to be recognized in the prestigious insideBIGDATA Impact50 List for Q4 2020. Arize AI is the leading ML Observability platform in the market designed to troubleshoot, monitor and explain AI deployed in the real world. With Arize AI, Data Scientists and Machine Learning engineers are able to deploy their models with confidence, creating a more transparent and trustworthy future with AI”  — Jason Lopatecki, CEO of Arize AI

About Arize AI

Arize AI was founded by leaders in the Machine Learning (ML) Infrastructure and analytics space to bring better visibility and performance management over AI. Arize AI built the first ML Observability platform to help make machine learning models work in production. We provide a real time platform to monitor, explain and troubleshoot model/data issues, as models move from research to real world.