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.

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