It’s happened again. You built another AI model that will never see the light of day because it won’t make it past the AI “valley of death” - the crossover of model development to model deployment across your enterprise. The handoff between data science and engineering teams is fraught with friction, outstanding questions around governance and accountability, and who is responsible for different parts of the pipeline and process. Even worse? The patchwork approach when building an AI pipeline leaves many organizations open to risks because of a lack of a holistic approach to security and monitoring.

Join us to learn about approaches and solutions for configuring a ModelOps pipeline that’s right for your organization. You’ll discover why it’s never too early to plan for operationalization of models, regardless of whether your organization has 1, 10, 100, or 1,000 models in production.

The discussion will also reveal the merits of an open container specification that allows you to easily package and deploy models in production from everywhere. Finally, new approaches for monitoring model drift and explainability will be revealed that will help manage expectations with business leaders all through a centralized AI software platform called Modzy®.


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About the speaker


Clayton Davis, Head of Data Science at Modzy

Clayton Davis is Head of Data Science at Modzy where he oversees model development, operational data science capability development, and AI research. Prior to his role at Modzy, Mr. Davis spent over 15 years leading data science work for commercial and government organizations. His experience has spanned the data science spectrum, from analytic macro creation to cloud based deep learning research and petabyte scale big data processing on Hadoop clusters. He has a passion for solving complex puzzles and holds a graduate degree in Physics.