Harnessing the power of machine learning (ML) for astrophysical discovery necessitates not only sophisticated models but also the implementation of robust operations, or MLOps. This talk will highlight the potential of MLOps to streamline the deployment of ML in astrophysics. We'll delve into the iterative cycle of data acquisition, model re-training, evaluation, deployment, and monitoring/telemetry — collectively forming the engine of successful AI ventures. We'll explore key MLOps practices from industry, emphasizing the critical role of experiment tracking, reproducibility, data/model provenance and versioning, and effective collaboration in this process.