Data science has made significant strides with tools like Jupyter Notebook, which simplify complex tasks by executing high-level code tailored to specific problems, thus enhancing productivity and encouraging experimentation. While data scientists often work locally, they leverage cloud resources for data storage and model analysis due to their scalability and cost-effectiveness. Despite the advantages of accessing cloud resources through SDKs like AWS's boto3, these require expertise in managing and architecting solutions. Pulumi's Automation API enhances this process by offering high-level abstractions that allow data scientists to focus more on their analyses and models without deep knowledge of cloud APIs. It simplifies infrastructure management by providing robust deployments, concurrency management, and state maintenance. An illustrative example within a Jupyter notebook demonstrates how to create and manage a static website using an S3 bucket through Pulumi's framework, showcasing the ease of automating infrastructure tasks alongside data science work. Pulumi allows data scientists to maintain their focus on analysis and model building in familiar environments by reducing the infrastructure management burden and facilitating seamless integration with existing workflows.