AI/ML development with Python & Jupyter

AI/ML development and inference using Python and Jupyter provides a powerful, interactive environment for building, training, testing, and deploying machine learning models. Python offers rich libraries for data processing and model development, while Jupyter Notebooks enable step-by-step experimentation, visualization, and reproducible workflows.

Features:

• Interactive development environment using Jupyter Notebook or JupyterLab.
• Rich Python ecosystem for data analysis, model training, and inference.
• Supports visualizations, experimentation, and reproducible research workflows.

AI/ML development & inference using Python and Jupyter Usage:

$ sudo su
$ cd /opt/ml 
$ source venv/bin/activate
$ To check all AI/ML dependencies version: pip list
$ Start Jupyter: jupyter lab --ip=0.0.0.0 --port=8888 --allow-root
$ After starting, you'll see output with a URL containing a token. Like: http://YOUR_SERVER_IP:8888/lab?token=YOUR_TOKEN_HERE
$ Copy that entire URL (including the token) and paste it into your browser to check working.
    

Disclaimer: Python, Jupyter, and associated AI/ML libraries are open-source projects maintained by their respective communities. They are provided "as is" without warranties of any kind. Users are responsible for ensuring compliance with licensing, security practices, and usage policies.