Get Your Code to be Modular, Robust, Easier to Maintain, Scalable, and Ready to Deploy
Join a live Workshop with David Coba, who will cover the following:
- What it means for a project to be production-ready
- Why and how to migrate from notebooks to packages
- Implementing automatic code-quality checks
- Serving machine learning applications with FastAPI
About the Workshop:
Join us for this 90-minute workshop covering best practices for developing production-ready machine learning applications with Python. You will discover the benefits of migrating from notebook-based workflows to using Python packages, resulting in code that is modular, robust, easier to maintain, scalable, and ready to deploy. After this session, you will understand how to use tools and libraries like Poetry, Ruff, Pytest, and FastAPI to make developing mature applications easier.
- When: April 11
- Time: 3:30 - 5 PM CET | 9:30 - 11 AM GMT-4
- Duration: 90 minutes
- Online Zoom
Meet the Speaker:
David Coba
David has a background in mathematical psychology, where he used statistics and data science to study human behavior. He also has a passion for software development and multiple years of experience teaching about technical topics.
This Session is for You If:
- You work regularly with Python in machine learning, data science, analytics, or related contexts.
- You want to learn best practices for developing, testing, and deploying scalable machine learning applications.
- You primarily work with notebooks but would like to learn how to structure your projects using Python packages.
Data Learning Week
Workshop Series
As part of Xebia Academy's Knowledge Sharing initiative, we have planned a series of workshops in the Data area to take your skills further. Check out the topics of other workshops in the series and sign up for the ones that interest you. We will have a special bonus from Xebia Academy for all workshop participants.
