Bridging the Gap: Mastering ML Debugging Beyond Tutorials!

This article highlights the exciting journey of a backend engineer transitioning into machine learning and the challenges of debugging. It emphasizes the need for practical debugging skills and understanding the probabilistic nature of ML models to build robust and effective solutions, showcasing a path for developers to level up their ML skills.

• •The core challenge identified is the transition from tutorial-based learning to debugging real-world ML models.
• •The author's experience highlights the difference between deterministic debugging in backend engineering and probabilistic debugging in ML.
• •The need for better tools and methods for debugging ML models is implicitly raised.