Chang Hsin Lee
Chang is a data scientist at Lowe's Home Improvement, where he had worked on data science projects on fraud detection and supply chain. He started his journey in machine learning recently as a baseball research intern for the Tampa Bay Rays. Chang likes to write blog posts about his thoughts on data, and is a co-organizer of the Davidson Machine Learning meetup in Charlotte, NC. Besides math and coding, Chang is a soul food advocate and survives on barbecue.
Friday 1:00 PM - 1:00 PM
B Track Atlantica B
There is a lot of hype around machine learning. There are numerous tutorials on how to train a machine learning model with existing libraries, which had made starting a project much easier. But how do I actually build a machine learning product?
As someone who is new to software development and machine learning, I have worked on several projects from scratch in the last two years. I also learned about additional considerations in building a machine learning product besides what's covered in software best practices.
In this introductory talk, I will provide a few tips in different stages of a machine learning project. These are things I wished I knew when I first started doing machine learning, such as what kind of questions to ask and how to structure my workflow, that will lead to better results and save time.