The AI industry's rapid growth, exemplified by ChatGPT's success, highlights a significant digital transformation, but implementing AI/ML in products faces challenges, particularly in managing cloud infrastructure. The process involves multiple steps like data preparation, model training, and building backend services, with cloud orchestration being the most complex part, often handled manually or with scripts, leading to inefficiencies. Infrastructure as code (IaC) is crucial for overcoming these challenges, offering a structured approach to managing cloud operations. Pulumi, an open-source IaC platform, allows engineers to manage AI infrastructure using familiar programming languages like Python. This blog post introduces a series on AI Infrastructure with Python, beginning with deploying a LLM model for a chatbot using Pulumi, showcasing its benefits in simplifying AI/ML tasks and enhancing development speed. The series aims to demonstrate how Pulumi and Python-based IaC can streamline and democratize AI development, encouraging collaboration within the MLOps community.