Will Python remain the core language for Machine Learning as automation scales up?

All answers to this question.

AutoML is fantastic for quick prototyping, but it rarely produces a model that is ready for a specialized production environment. automation in ML helps with hyperparameter tuning, but the data cleaning and feature engineering still require a human with a Python script. We are far from a world where a "black box" can handle the nuances of raw, messy real-world data. Learning Python ensures you can look under the hood and fix the model when the automated version fails to meet accuracy standards.

   Answered 2025-05-17 by Rebecca Sharp

Do you think we will eventually reach a point where the "human in the loop" is removed?

   Answered 2025-05-19 by George Harrison

• George, that is the dream for some, but the reality is that edge cases always exist. automation handles the average, but Python-skilled engineers handle the exceptions. As long as there is unique data, there will be a need for custom Python development in the ML lifecycle.

     Commented 2025-05-21 by Larry King

Python's flexibility makes it perfect for the "messy" parts of machine learning.

   Answered 2025-05-23 by Martha Stewart

• Well said, Martha. The "messy" parts are exactly where the most value is created in any ML project.

     Commented 2025-05-25 by Rebecca Sharp