Building data science products? Think business first!

Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.

Outline:
• Why starting with technology is the wrong approach? Ask a business question and work your way backward
• Choosing the right machine learning algorithm for your business problem
• High-level thinking process in conceiving, implementing, deploying, and maintaining a machine learning system

0:00 Introduction
0:46 About the Speaker
1:10 Why This Talk?
2:05 Is a Highly Accurate Model Always Desirable?
2:23 The 'Data' Question
3:11 The 'Metric' Question
4:43 The 'Repeatability Question
6:31 The 'Business' Question
7:39 The 'Impact' Question
11:16 Modern ML Tools - A Blessing or a Curse?
13:20 Machine Learning Model Life Cycle
22:11 What is your biggest pain point?
23:07 Data beats algorithm
24:07 Dispelling a Common Myth
24:29 The 80/20 rule
25:09 Beg, borrow and steal
25:44 Building the model is only a small piece of the puzzle
26:43 The Importance of Data
27:45 Predictability of your model
27:56 Further Evaluation Data Set
29:23 Increasing the exposure of your model

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