Description
This talk explores using VS Code and its extensions as the central orchestrator for collaborative machine learning workflows, enabling scalable data science projects
Abstract
This session dives into collaborative machine learning (ML), specifically how to leverage VS Code as the core orchestrator for managing data science workflows. Using VS Code's strengths in code editing, debugging, and version control, data scientists can streamline collaboration and ensure project scalability.
The talk will explore VS Code's key features and functionalities that empower effective collaboration in ML, along with best practices for setting up and managing collaborative workflows within this versatile platform.
In this workshop, we will learn:
- My Story from an Individual working environment to a collaborative data science environment.
- Reasons why VS Code might be a better choice for many data scientists and ML Engineers than Jupyter Notebook.
- Software engineering best practices for data scientists and ML engineers.
- Several VS Code Extensions for Data Scientists and ML Engineers and their functionalities.
Audience level: Intermediate or Advanced