Recently, Python engineers have more opportunities to work with data scientists than before. At the same time, they are often faced with the research-oriented code written by researchers or data scientists. In order to integrate this code with systems such as APIs, python engineers need to additionally write the code or refactor it, and make them work on the server.
This tutorial will provide chances to experience the whole process from analysis to API development by using python. In more detail, this tutorial covers the gap between the research-oriented code and production code of machine learning APIs. What is the gap between them? How it can be implemented based on real-world python code? How can the code validate whether the dataset is correct? How can machine learning models be continuously inspected? Audiences can earn the answers to these questions from this tutorial.
Jesse is a software engineer working at an EdTech company in Tokyo. He has been developing a recommender engine which can optimize appropriate learning materials according to learner's abilities. Prior to this, he used to research the relationships between online learning behaviors and learning outcomes at the UCL Institute of Education (IOE) in the UK. His interest is in how to bridge the gap between data science and engineering.