Central ML teams were first created to help build and manage ML platform tools for data science and machine learning professionals at top technology companies, usually with the goal of providing a unified and standardized experience for ML application development. As the field of MLOps matures, however, many are questioning whether this type of team structure is right for their organization. Meta, for example, recently moved from a centralized approach to one where most former ML platform teams are embedded in product organizations.
To share what it takes to build a central ML team that endures, Arize is assembling a panel of experts who are leading successful central ML initiatives and teams. In this mini-event moderated by Claire Longo – Arize AI’s customer success lead and an MLE alum of Twilio and Opendoor – you will hear from: