Are .ipynb notebooks already outdated in the agentic era? [D]

Key points

• I am a data scientist who started working in the industry before the LLM revolution.1
• Especially in classical ML applications, where you still need to explore data, run experiments, check different hypotheses and decide what to do next based on the results.1
• But why do we still need to organise the whole workflow around code cells?1
• Are ipynbs still good enough, or are we just used to working this way?1

Sources (1)

• [1]Are .ipynb notebooks already outdated in the agentic era? [D]
  r/MachineLearning (top, daily) · Oct 10, 10:51 AM

  Back then, Jupyter Notebooks were a perfect fit for the classical DS pipeline: EDA -> data prep -> fit -> eval -> tune -> save model artefact and notebook.

  I am a data scientist who started working in the industry before the LLM revolution.

Extractive summary: sentences quoted from the sources.