Organizations aiming for effective long-term machine learning (ML) investments are creating custom workflows tailored to their specific data and outcomes, with an emphasis on keeping models updated and trained on the latest data. This tutorial illustrates how to automate ML workflows using CircleCI, breaking them down into steps for continuous integration (CI) and continuous deployment (CD), which include building, training, testing, deploying, and retraining models. It highlights how CircleCI's platform facilitates automation, allowing ML models to be updated and validated automatically, thereby freeing experts from manual tasks and enabling them to focus on developing more accurate models. Additionally, it discusses the integration of GPU resources for efficient model training and the importance of MLOps strategies to ensure the models remain effective through regular updates and testing. The tutorial also mentions the use of CircleCI's web console for monitoring workflows and approving model updates, and the flexibility it provides in configuring custom notifications and leveraging both cloud and on-site processing power.