Introducing ModelOps: How to deploy and manage every type of model

To be competitive, organizations have to put more models into production more rapidly than ever before. At the same time, data scientists have to ensure trust, repeatability and transparency in those models. ModelOps, a superset of MLOps, gives data scientists the capabilities they need to adapt quickly while maintaining model integrity. In this talk we will address current challenges organizations face developing and deploying AI and analytics. Then, we’ll share how ModelOps plays an important role in helping solve these problems and operationalize analytics at scale. Finally, Marinela Profi will share recommendations on how you can get started today and implement ModelOps to overcome the barriers you’re facing.
Who is this presentation for?
Data Scientists, Machine Learning Engineers, SAS and open source users
 

Data Scientist specialized in Advanced Analytics techniques, such a Forecasting and ML. I have a bachelor’s in economics, a master’s in statistics and an MBA. Over the past 5 years, I worked as an analytic and AI consultant. I honed my skills mining data, developing models and technical/business solutions, including deploying AI at scale. My experience spans banking, insurance, manufacturing, retails and health care. Currently I am a Data Science Marketing Manager at SAS, where I use my cross-domain expertise in statistics, business and marketing, to drive recognition on MLOps, open source software and data science . I am a keynote speaker and presenter at different global conferences, where I share trend and priorities of the data science industry. I am a published author, contributor to several eBooks, and blog writer on major industry and data science blogs.