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