Agile + DevOps East 2022 Concurrent Session : MLOps for Agile Data Science at Scale

SEE PRICING & PACKAGES

Wednesday, November 9, 2022 - 10:30am to 11:30am

MLOps for Agile Data Science at Scale

Agile organizations have been successful in improving collaboration and reducing waste in software development. They have also learned to automate and streamline their software delivery process. But many teams are still struggling to leverage the same agile principles to their artificial intelligence (AI) initiatives. Operationalization of Machine Learning (ML) models at scale is an increasing challenge and a barrier to AI adoption for many companies. Join JL Marechaux as he explores how Data Scientists, ML Engineers, and Operations teams can leverage DevOps practices to deliver machine learning systems. JL will identify the prerequisites for successful enterprise AI initiatives and describe the different MLOps processes to support production-grade ML systems. With specific examples from a Google MLOps framework, JL will focus on MLOps capabilities and maturity levels for AI teams. Discover the specificities of an AI project and how MLOps differs from DevOps. Learn what MLOps is, why it is needed for ML at scale, and how MLOps can increase AI team velocity and reduce time-to-value. Finally, see how you can adopt MLOps to support continuous flows in your end-to-end machine learning initiatives.

Interests

Google

JL Marechaux is a Marketing Data Science Lead at Google where he helps North American companies leverage artificial intelligence for their digital transformation. In his current role, JL specializes in advanced analytics and machine learning applied to digital marketing use cases. His professional background is in software engineering, agile practices and cloud computing. Prior to joining Google, JL had a focus on AI-centric applications for the supply chain industry. He also spent many years in large tech companies like IBM, BlueYonder and Sybase (now SAP) where he held multiple positions in software engineering and architecture. JL is passionate about AI/ML, technologies and loves to meet people to share ideas and experiences.