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Machine Learning Model Life Cycle
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Classroom Contents
Building Data Science Products - Think Business First
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- 1 Intro
- 2 About the Speaker
- 3 Why This Talk?
- 4 Is a Highly Accurate Model Always Desirable?
- 5 The 'Data' Question
- 6 The 'Metric' Question
- 7 The 'Repeatability Question
- 8 The 'Business' Question
- 9 The 'Impact' Question
- 10 Modern ML Tools - A Blessing or a Curse?
- 11 Machine Learning Model Life Cycle
- 12 What is your biggest pain point?
- 13 Data beats algorithm
- 14 Dispelling a Common Myth
- 15 The 80/20 rule
- 16 Beg, borrow and steal
- 17 Building the model is only a small piece of the puzzle
- 18 The Importance of Data
- 19 Predictability of your model
- 20 Further Evaluation Data Set
- 21 Increasing the exposure of your model
