How to Transform Research Oriented Code into Machine Learning APIs with Python (Tetsuya Jesse Hirata) (Japan) is an English session in the online PyCon HK 2020 Spring.
- The audiences can be engineers, data scientists, or researchers at junior or middle level who use python.
- This talk could be more beneficial for people who have experience to be involved with AI / ML projects.
- One of the major things that the talk can provide could be that audiences would learn each responsibility of each role in AI / ML projects and process to implement AI / ML products, and the better or best practices of ML API implementation.
- Why do I talk about this topic ? (1 minute)
- The Four Steps to transform Research Oriented Code into Machine Learning APIs with Python (1 minute)
Main Talk (+ sample codes and explanations in each section)
1) Understand what code you look at and make, and how to handle the code. (6 minutes)
- What is Research Oriented Code ?
- What are ML APIs ?
- How should engineers handle research oriented code ?
2) Modularize research oriented code (6 minutes)
- Categorize research oriented code into preparation code, preprocessing code, and ML code
- Break them out into functions and make them testable
- Clarify input and output of the code, and define URI
3) Refactor research oriented code (6 minutes)
- Prepare for refactoring by understanding requirements of each code and taking notes about them
- Simplify I/O code in preparation code
- Transform coding style with pandas into purely pythonic code in preprocessing code
4) Check the ML APIs can work correctly on the server (6 minutes)
- Write decorators to automatically check parameters
- Set up production-like environments
- Suggest a tiny example of CI/CD environments for ML APIs.
Summarize the talk
- Quickly review the four steps to transform Research Oriented Code into Machine Learning APIs with Python (1 minute)
- Share the useful resources and tips related to what is mentioned in the talk. (1 minute)
Speaker Bio: Tetsuya Jesse Hirata
Jesse is a software engineer based in Tokyo. He has been involved with several AI/ML projects in EdTech domain and has mainly been implementing ML APIs and ML Ops environment. 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.
Session Time in HKT: 9:00 PM on 10 May 2020 Sunday.
Session Time in GMT: 1:00 PM on 10 May 2020 Sunday.