Machine Learning and AI Tools

Jupyter Notebook is a cornerstone tool in data science and machine learning workflows, enabling interactive, literate programming in Python, R, and other languages. It integrates code, output, visualizations, and narrative in a single document. However, as notebooks scale in size and complexity or are integrated into collaborative and production settings, advanced users often encounter issues such as "kernel crashes, execution hangs, environment dependency mismatches, notebook version control conflicts, and resource contention during parallel execution". This article provides a detailed troubleshooting guide to address these issues in high-performance and collaborative Jupyter environments.

PyCaret is a low-code machine learning library built on top of popular Python libraries such as scikit-learn, XGBoost, LightGBM, and others. It enables quick experimentation, model comparison, and deployment workflows with minimal code. However, in enterprise or large-scale ML projects, developers often encounter advanced issues such as "memory bottlenecks, model comparison inconsistencies, environment dependency clashes, parallel processing failures, and integration challenges with MLflow or cloud services". This article provides an in-depth troubleshooting guide for stabilizing and scaling PyCaret-based workflows in production and collaborative data science environments.