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Hybrid Computing and Deploying Machine Learning Models in Production
This chapter delves into the concept of hybrid computing, where work is done on a laptop while leveraging the power of the cloud for large scale computing. The speakers discuss the challenges and importance of deploying machine learning models into production, emphasizing reproducibility, scalability, and cost efficiency. They also touch on the significance of maintaining robustness in data programs and models, and strategies for improving reliability in data science organizations.