This comprehensive tutorial explores the integration of CircleCI runners with Scaleway's cloud ecosystem to efficiently manage AI/ML workflows, utilizing CI/CD practices and scalable GPU instances. By leveraging CircleCI's automation and ephemeral runners, the tutorial demonstrates cost-effective ways to provision, train, and deploy models while maintaining infrastructure flexibility. It emphasizes the benefits of using Pulumi for Infrastructure as Code to streamline resource provisioning and highlights the importance of configuring environments with the appropriate secrets and environment variables. The guide also covers practical steps for setting up models on scalable cloud infrastructure, including Docker installation for model serving, and the orchestration of AI/ML jobs using CircleCI's pipeline. The tutorial concludes with the importance of cleaning up resources to maintain cost efficiency and outlines how these practices can be applied to other cloud environments beyond Scaleway.