What is the most difficult part of the machine learning process? Data collection? Feature Engineering? Model selection and tuning? Deploy and monitoring? What if you have a whole bunch of models, and business requires you to continuously improve, experiment, re-train and integrate models? And what if you are not even a Data Scientist?

In this talk:

How to not be drown in chaos, and build structured ML-integration process in a large company
Taking a close look at what can be automated (spoiler: everything)
Discussing "conveyor" taking ideas as input can make a great impact on business metrics, through fast and convenient machine learning integration
What can we achieve by using very basic and simple models


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