Getting predictions in Jupyter Notebook is sufficient till you are in Campus, but what after getting into Corporate? The model developed once has to reach clients in order to benefit the company. Also, it should be capable of scaling and learning from user generated data as and when required.
Learn about all the necessary Data Science and Engineering skills, responsibilities, and best practices for becoming a (Marketable) Machine Learning Engineer in this session which will walk you through the "Design - Develop - Deploy" cycle.
In a nutshell, this interactive session with group activities, and discussions will nurture an ML Enthusiast turning into an ML Engineer.