In past several years, AI and Analytics have revolutionized business and IT operating models through intelligent automation using machine language model. It fostered enterprise agility, creativity, and innovation unlocking key competitive advantages.

MLOps practices extend on Agile and DevOps nurtured and matured in past two decades, applied to AI/ML model development, testing, deployment, monitoring, and maintenance lifecycle.

With unlimited new use cases, an enterprise need to upskill and ramp-up, to experiment and monetize these AI opportunities.
In this talk, we will discuss how MLOps is natural progression from Agile and DevOps. Review the MLOps lifecycle, focusing on similarities and differences. Conceptual MLOps team and architecture design.

This talk will conclude with best-in-class principles, and initial roadmap to get started with MLOps journey in their companies.