The text discusses the challenges data science teams face in managing CI/CD infrastructure costs, which can quickly escalate due to resource-intensive model training, large dataset handling, and complex ML pipeline requirements. Many teams struggle to balance rapid experimentation with resource efficiency, leading to excessive compute costs, delayed model deployments, and fragmented experiment tracking. To address these challenges, the text highlights the importance of optimizing pipelines through efficient data handling, compute usage, testing, and experiment management. CircleCI is presented as a solution tailored for data science and ML workflows, offering scalable infrastructure, efficient resource management, and automated testing to streamline ML pipelines and reduce infrastructure overhead. By using CircleCI, teams can dynamically scale compute, optimize ML environments, manage large datasets, monitor resource usage, automate deployments, and enhance security, ultimately allowing them to focus on improving models rather than managing infrastructure.