Machine learning (ML) has rapidly become essential for businesses across various industries due to its ability to continuously learn from new data and revisit historical data with advanced tools. As ML deployment expands, maintaining updated models and integrating new data can strain tech teams. This tutorial introduces building a continuous integration (CI) pipeline to automate ML workflows using CircleCI, highlighting steps like building, training, testing, and packaging models. It emphasizes the benefits of breaking down workflows into automated stages triggered by code changes, utilizing cloud-hosted GPU resources to enhance performance, and integrating with existing tools via Git. The example provided involves a TensorFlow image recognition model using Keras and MNIST data, illustrating how CircleCI can manage ML tasks regardless of the complexity or platform. The discussion extends to using CircleCI's cloud compute resources and the advantages of GPU usage for processing-intensive ML tasks. The tutorial underscores the importance of CI/CD pipelines in improving ML outcomes and the potential for automating various processes beyond traditional software, encouraging users to explore CircleCI for developing their MLOps workflows.