Generative AI is transforming industries, but for many enterprises, the journey from proof of concept to production remains a major hurdle. While businesses are investing heavily in building AI-powered applications like RAG-based chatbots, the vast majority of these projects fail to deliver tangible results. Success demands more than experimentation–it requires a deeper understanding of the challenges of managing AI in production and adopting MLOps practices to streamline the process. This report explores how enterprises can leverage MLOps, with a Kubernetes-first approach, to overcome adoption barriers, scale AI effectively, and maximize business impact. From building responsible models to running reliable production systems, our guide offers the strategies and tools you need to thrive in an AI-driven competitive landscape. With the proliferation of AI ML and an ever-increasing number of models being created, MLOps has emerged as a new paradigm for delivering high-quality models to production quickly, applying DevOps principles to AI models and AI-powered applications instead of traditional software applications. However, MLOps doesn’t just apply DevOps principles to the AI development lifecycle but builds upon them to define foundational best practices for building and running AI-powered applications.

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