Collaborative ML Using VS Code Extensions for DS/ML Teams Presented by: Gift Ojeabulu

I'M Gift O, Co-founder of Data Community Africa ML Developer Relations at dvc.ai, Your friendly neighborhood AI & Python Advocate: Making AI Less Scary with More Wakanda and Steeze. X/Twitter: GiftOjeabulu_ Linkedln: Gift Ojeabulu Data Scientist & Technical Writer: Basically a PowerPoint Jedi. Warning: May Cause Data-Driven Decisions. Others:DevNetwork Advisory board member, AWS ML CM Builder, MLOps Community Lagos lead

01 02 03 04 05 06 My Story: Essence of collaboration Jupyter Notebook & VS Code. VS Code Extensions for Data scientist and ML Engineer. Stackoverflow Developer 2023 Survey and result. VSCode extension overview & SWE principles for Data Scientists. Conclusion and Questions TODAY'S AGENDA What we'll learn:

1 Learning from courses and Youtube Videos Phase 1 My Story 2 Zindi competitions and participating in onsite Hackathons Phase 2 3 My first job as a sport data scientist. Collaborative environment Phase 3

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Survey StackOverflow 2023 Survey Green represent VS Code The item represent several IDEs in a Bar Chart voted by over 86000 experienced(85%) and entry level(15%) developers.

Results ● Visual Studio Code remains the preferred IDE across all developers, increasing its use among those learning to code compared to professional developers (78% vs. 74%). ● 2 out of 5 ● The item represent several IDEs in a Bar Chart voted by over 86000 experienced(85%) and entry level(15%) developers. 85.99%

Tip: Collaboration makes teamwork easier! Others: ZenML Studio still in preview stage VS Code Python Jupyter Notebook renderer Jupyter Python Indent Gitlens DVC Pylance Error lens Data Wrangler Sand Dance GithHub Co-pilot Liveshare F Why VS Code?

Overview Following best practices in your “code kitchen” helps you create well-ordered code means that you and others can pick up the analytics project in the future, understand, extend and reuse it. Proponents Project structure, reproducible environment, clean code (e,g testing) and version control SWE principles for Data Scientist

Methodology Step 1 Step 2 Visit the VS Code Marketplace . Use the filter options on the left side of the page. You can filter by category (e.g., Data Science, Machine Learning) and sort by “Date” (newest first).

Analysis and Conclusion VS Code extensions, when combined with a commitment to software engineering principles, can transform your data science and ML team’s collaborative workflows You will create a synergy that propels your projects to new heights by encouraging transparency, communication, efficiency, and maintainability. VS Code is ideal for complex projects, collaboration, large codebases, and professional data science projects requiring a robust development environment, version control, and scalability for handling large codebases.

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