Mastering Responsible Machine Learning in an Open World
Mastering Responsible Machine Learning in an Open World


Machine Learning and AI is already ubiquitous today, touching many moments of our daily lives. From the mundane and bizarre to life-saving and socially, significant improvements. In this climate of increasing AI adoption, ethics has become a critical concern among consumers, auditors and governments. We observe a move from “How can Machine Learning help my business?” to “How can I design and deploy Machine Learning solutions at scale and responsibility?”


Tamara Fischer, a graduate statistician, has been working for many years in the role of Principal Solutions Architect, Analytics, at SAS in the DACH region. In this role she implemented solutions along the entire analytical life cycle from model development to model deployment. Currently she is working in an international team of Data Scientists and Architects which takes care for the EMEA region. Her work is focused on all analytical aspects of ModelOps. A term that SAS refers to a wider spectrum of models - data driven and business driven – that can be combined for decision-making to drive better business outcomes, a spectrum in which SAS delivers capabilities across

Open Data Science




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