‌In the dynamic world of machine learning, efficiently scaling solutions from research to production is crucial. In this session, we explore the nuances of scaling machine learning pipelines, emphasizing the role of containerization in improving reproducibility, portability and scalability. Key topics include building efficient training pipelines, monitoring models in production and optimizing costs while handling peak loads. Attendees will learn practical strategies for bridging the gap between research and production, ensuring consistent performance and rapid iteration cycles. Tailored for professionals, this presentation delivers actionable insights to enhance the scalability and robustness of ML systems across diverse applications.