ML Team Leader Q&A Part 4 🎦 Traditional Software vs ML Software management (3min read)

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Exploring the Frontier: A Comparative Analysis of Machine Learning vs. Traditional Software Development Paradigms 🚀

Hey there, tech enthusiasts! 🎉 Today, we're diving into the fascinating world of machine learning (ML) and traditional software development. We'll explore the insights shared by the renowned Andrew Ng, and I'll sprinkle in my own experiences to make this a thrilling ride. Buckle up! 🎢

1. Time Percentage Difference: ML vs. Traditional Software Development 🕒

Machine learning development is like a wild adventure, filled with unexpected twists and turns. Unlike traditional software development, where you might spend a significant amount of time writing code and then debugging, ML is all about rapid prototyping and iterative debugging. 🛠️

Traditional Software Development: Spec ➡️ Code ➡️ Debug (Time split: 40% development, 60% debugging)

ML Software Development: Prototype ➡️ Analyze ➡️ Debug (Time split: 20% development, 80% debugging)

The shift in time allocation emphasizes the exploratory nature of ML, where the unknowns in data and model behavior require more attention and fine-tuning.

2. The Role of Tech Lead and Engineering Manager in Two Paradigms 🧠

In both paradigms, the tech lead and engineering manager play crucial roles, but their focus shifts:

Traditional Software Development: They oversee the alignment with product specs, code quality, and ensure the team follows best practices.

ML Software Development: They guide the team in rapid prototyping, error analysis, and iterative improvement. Their experience helps in identifying anomalies and steering the project in the right direction.

3. Engaging Key Stakeholders in These 2 Paradigms 🤝

Stakeholder engagement varies between these paradigms:

Traditional Software Development: Clear specs and milestones make it easier to keep stakeholders informed and aligned.

ML Software Development: The iterative nature requires continuous communication and collaboration with stakeholders. Regular updates and transparency about challenges and discoveries are key.

4. Rewarding and Penalizing Individual Software in These 2 Paradigms 🏆

The approach to rewards and penalties must adapt to the unique characteristics of each paradigm:

Traditional Software Development: Rewards can be tied to meeting specifications, deadlines, and code quality. Penalties might relate to delays or deviations from the plan.

ML Software Development: Rewards should encourage exploration, innovation, and problem-solving. Penalties, if necessary, should be carefully considered, recognizing that the iterative and experimental nature of ML can lead to unexpected outcomes.

Conclusion 🌟

Machine learning development is a thrilling journey filled with surprises. It contrasts sharply with traditional software development in terms of time allocation, leadership roles, stakeholder engagement, and reward systems. By understanding these differences, we can navigate the unique challenges and opportunities that each paradigm presents. Keep exploring, keep learning, and let's build the future together! 🚀

Happy coding! 🎉 Your tech-savvy friend, Ray

🔗 Link to Andrew’s letter

https://www.deeplearning.ai/the-batch/building-machine-learning-systems-is-more-debugging-than-development/