From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams

Author(s): Gift Ojeabulu

Originally published on Towards AI.

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams

In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers. We will discuss why VS Code may be a superior choice compared to Jupyter Notebooks, especially in team settings.

Outline

• The Essence of Collaboration: From an Individual Working Environment to a Collaborative Data Science Environment.
• Why VS Code might be better for many data scientists and ML engineers than Jupyter Notebook.
• Essential VS Code Extensions for Data Scientists and ML Engineers.
• Factors Influencing the Choice Between Jupyter Notebooks and VS Code
• How to find new extensions for vs code for data science and machine learning.
• Conclusion.

My story (The Shift from Jupyter Notebooks to VS Code)

Throughout early to mid-2019, when I started my data science career, Jupyter Notebooks were my constant companions. Because of its interactive features, it’s ideal for learning and teaching, prototypes, exploratory data analysis projects, and visualizations. Think of them as digital scratchpads perfect for participating in Kaggle and Zindi competitions, creating data visualizations, and working directly with the data.

But things got complicated when I landed my first real data science gig and transitioned into a team environment.

Imagine the scene

You have spent hours crafting a beautiful analysis in your notebook, a perfect marriage of code, and insightful commentary. You share it with the team, brimming with excitement, only to be frustrated. They cannot replicate your stellar results because of environment inconsistencies, missing libraries, and many other reasons.

Sharing bulky zip files containing notebooks, scripts, and datasets became a logistical nightmare. Reproducing results on different machines felt like alchemy; it was a frustrating guessing game with a cryptic mix of environment variables and missing dependencies that could frustrate even the most solid or experienced data scientist.

“Did I install that library in the right virtual environment again?”

This wasn’t uncommon. Many beginner data scientists, myself included back then, struggled with the shift from solo exploration to collaborative, production-ready workflows.

We are data wranglers at heart, not necessarily software engineers by training, and best practices for reproducibility can sometimes get pushed aside in the heat of exploration.

Well, it seems cool, but the above is a recipe for collaboration chaos.

This experience highlighted the importance of seamless collaboration and reproducibility in data science teams. As a result, I turned to VS Code, which offers a more robust environment for teamwork and adherence to software engineering principles.

In my case, I found a solution for a larger team setting: VS Code.

Having explored various IDEs, I confidently recommend VS Code as a better option for Jupyter Notebooks regarding collaboration, following software engineering principles as a data scientist and machine learning engineer, and working with teams.

Compelling reasons why VS Code might be a better choice for many data scientists and ML Engineers than Jupyter Notebook working in teams

Here’s a comparison between VS Code and Jupyter Notebook for data scientists and ML engineers in a collaborative environment:

These differences highlight how VS Code, with its extensive customization and integration options, can be a more efficient choice for many data scientists and ML engineers compared to Jupyter Notebook.

In this section, we will learn about the VS code extensions that are essential to my workspace and adhere to key software engineering principles.

Here’s a glimpse at the list:

• Python
• Pylance
• Jupyter
• Jupyter Notebook Renderer
• Gitlens
• Python Indent
• DVC
• Error lens
• GitHub Co-pilot
• Data Wrangler
• ZenML Studio
• Kedro
• SandDance

1. Python Extension

The Python extension is crucial for efficient development, providing functionalities such as:

• Linting and Syntax Checking: Helps identify errors in your code.
• Debugging and Code Navigation: Streamlines the debugging process and allows easy navigation through your codebase.
• Auto-Completion and Refactoring: Enhances coding efficiency and readability.
• Unit Testing Integration: Facilitates testing practices within your projects.

This extension also automatically installs Pylance, which enhances the experience when working with Python files and Jupyter Notebooks.

2. Jupyter Extension

The Jupyter extension integrates the power of Jupyter notebooks into VS Code, offering:

• Faster Loading Times: Improves the responsiveness of notebooks.
• Seamless Integration: Allows you to work within the familiar VS Code environment while leveraging Jupyter’s capabilities.
• Support for Multiple Languages: Basic notebook support for various programming languages enhances versatility.

3. Jupyter Notebook Renderer

This Jupyter Notebook Renderer allows you to view the outputs of your code directly within VS Code, eliminating the need to switch between windows. It enables dynamic updates of charts and graphs, detailed image previews, and interactive data visualizations, significantly enhancing the data exploration experience.

4. Python Indent

Proper indentation is vital in Python programming. The Python Indent extension automates indentation management, ensuring that your code adheres to best practices. It highlights potential indentation errors as you code, promoting readability and maintainability.

5. DVC (Data Version Control)

The DVC extension transforms VS Code into a centralized hub for all your machine learning experimentation needs. For data scientists and ML engineers, the road to breakthrough models is often paved with countless experiments and data iterations. Without proper management, this process can quickly spiral into chaos.

Key Features:

• Comprehensive Versioning: Beyond just data, DVC versions metadata, plots, models, and entire ML pipelines.
• Advanced Experiment Tracking: Record code, data, parameters, and metrics. Easily compare and identify top-performing models.
• User-Friendly Interface: Includes a dashboard, live tracking, and GUI-based data management.
• Large File Handling: Simplifies and streamlines versioning of large files, a common pain point in ML projects.
• Real-time Monitoring: Watch metrics evolve live, enabling rapid adjustments during training.

6. Error Lens

Error lens enhances the visibility of errors and warnings in your code, providing inline diagnostic messages. This feature helps developers catch issues early, making the development process more efficient and reducing the time spent debugging.

7. GitLens

Version control is essential for collaborative projects. Gitlens integrates Git functionality within VS Code, allowing you to visualize Git history, understand code authorship, and navigate through branches and commits. This extension simplifies collaboration and helps prevent potential conflicts.

8. Data Wrangler

The Data Wrangler extension offers an interactive interface for exploring, cleaning, and visualizing data. It generates Python code using Pandas as you work, making data manipulation efficient and code-friendly. This tool is invaluable for preparing data for further analysis.

9. ZenML Studio

ZenML Studio is a new extension that simplifies working with ZenML for MLOps projects. It integrates seamlessly with VS Code, providing a smooth experience for managing machine learning workflows.

10. Live Share

Live Share enables real-time collaborative development, allowing team members to co-edit and debug code together. This feature enhances the traditional pair programming experience by allowing developers to maintain their preferred settings while collaborating.

11. Kedro

The Kedro extension for Visual Studio Code integrates the powerful Kedro framework, enhancing project management and collaboration for data scientists and machine learning engineers.

Key Features

• Streamlines the organization of code, data, and configurations within Kedro projects.
• Enhances teamwork by providing features that allow multiple users to work on the same project efficiently.
• Pipeline Visualization.
• Code Quality and Testing.

12. SandDance:

Perfect for both data novices and seasoned analysts, SandDance shines when you’re facing a new dataset and need to quickly grasp its essence. Its ability to reveal relationships between variables and highlight trends makes it an invaluable tool for initial data exploration and hypothesis generation.

Factors Influencing the Choice Between Jupyter Notebooks and VS Code

While VS Code offers numerous advantages for data science teams, the optimal choice between Jupyter Notebooks and VS Code depends on various factors:

Team Size

Small teams: Jupyter Notebooks can be sufficient for very small, closely-knit teams where communication is frequent and informal. The interactive nature can facilitate rapid prototyping and experimentation.

Large teams: VS Code’s version control integration, code organization, and debugging capabilities become increasingly valuable as team size grows. It promotes code standardization and reduces the risk of errors.

Project Complexity

Simple projects: Jupyter Notebooks can handle exploratory data analysis and small-scale modeling projects effectively.

Complex projects: VS Code’s structured approach, debugging tools, and integration with other development tools are better suited for large-scale, production-oriented projects with multiple dependencies and complex workflows.

Individual Preferences

Interactive exploration: Data scientists who prefer an interactive, exploratory style may lean towards Jupyter Notebooks.

Code-centric workflow: Those who prioritize code organization, reusability, and collaboration may find VS Code more appealing.

Ultimately, the best approach often involves a hybrid strategy, leveraging the strengths of both environments. VS Code stands out as an ideal environment for complex data science projects that involve development, testing, and deployment, providing robust tools for collaboration and version control while still allowing for the interactive exploration capabilities of Jupyter Notebooks.

Finding New Extensions

To stay updated on the latest VS Code extensions, follow these steps:

1. Visit the VS Code Marketplace
2. Use the filter options to explore categories like Data Science and Machine Learning.
3. Sort by “Date” to find the newest extensions.

Conclusion

In summary, adopting Visual Studio Code (VS Code) along with its diverse extensions can significantly enhance collaboration for data science and machine learning teams.

Transitioning from Jupyter Notebooks to VS Code is not just a change in tools; it signifies a shift towards software engineering best practices that improve teamwork, reproducibility, and project management.VS Code’s features, including integrated version control and real-time collaboration tools, streamline workflows and minimize common collaborative challenges.

While Jupyter Notebooks excel in interactive exploration, VS Code offers a more structured approach suitable for complex projects. Ultimately, the decision between the two should align with the team’s specific needs, but for those aiming for a more collaborative and organized workflow, VS Code proves to be a superior choice.

Connect with me on LinkedIn

Connect with me on Twitter

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI