Multiuser Python Jupyter Notebooks for AI, ML & DS

Multiuser Python Jupyter Notebooks for Gen AI, ML & DS

Harnessing the Power of JupyterHub for Generative AI, ML & Data Science

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Last Updated: 2024-06-11 11:06:57 English Course Type: Self Learning

Harness the collaborative power of Python Jupyter Notebooks for generative AI, ML, and data science projects.
Set up and manage multiuser environments using JupyterHub on cloud platforms like AWS, GCP, and Azure.
Deploy Jupyter Notebooks on AWS, GCP, and Azure, enabling seamless collaboration with team members.
Enable ChatUI within Jupyter environments for real-time communication and collaboration.
Utilize magic commands to enhance productivity and efficiency in Jupyter Notebooks.
Secure JupyterHub deployments with HTTPS encryption to protect sensitive data.
Install and manage additional Python packages and dependencies within Jupyter Notebooks.
Gain hands-on experience through practical demonstrations, interactive exercises, and immersive lectures.

This comprehensive course equips participants with essential skills to harness the collaborative power of Python Jupyter Notebooks for generative AI, machine learning (ML), and data science projects. Through immersive hands-on exercises and practical demonstrations, learners navigate the dynamic realm of Jupyter Notebooks, gaining mastery over collaborative workflows and innovative techniques.

The course begins with an overview of its structure, objectives, and expected outcomes, emphasizing the importance of collaborative environments in data-driven projects. Participants delve into the core concepts and functionalities of Jupyter Notebooks in the context of generative AI, exploring intuitive interfaces and configurations tailored for AI applications.

Practical sessions guide participants through the setup and configuration of Jupyter Notebooks on cloud platforms such as AWS, GCP, and Azure, enabling seamless collaboration with team members. Advanced topics include enabling multiuser environments using JupyterHub, integrating ChatUI for real-time communication, and leveraging magic commands to enhance productivity.

Participants learn to secure JupyterHub deployments with HTTPS encryption, protecting sensitive data from unauthorized access. Additionally, they gain proficiency in installing and managing additional Python packages and dependencies within Jupyter Notebooks, extending the functionality of their environments.

By the course's conclusion, participants have acquired profound insights and practical skills essential for navigating the complex landscape of data-driven innovation. Whether data scientists, machine learning engineers, project managers, or enthusiasts, learners emerge ready to leverage Python Jupyter Notebooks for collaborative AI, ML, and data science projects.

Basic understanding of Python programming language and familiarity with data science concepts.
Prior knowledge of Jupyter Notebooks is beneficial but not mandatory, as the course covers introductory to advanced topics.
Familiarity with cloud computing platforms such as AWS, GCP, or Azure is advantageous but not required, as the course provides step-by-step guidance for setting up Jupyter environments on these platforms.

Data scientists, machine learning engineers, and AI practitioners seeking to enhance collaboration and productivity in their projects.
Professionals interested in exploring the collaborative capabilities of Python Jupyter Notebooks for generative AI, ML, and data science.
Team leads or project managers aiming to facilitate teamwork and innovation within their organizations by leveraging JupyterHub for multiuser environments.
Students or enthusiasts eager to delve into the dynamic realm of generative AI, ML, and data science collaboration using cutting-edge tools and technologies like Jupyter Notebooks and JupyterHub.

Section 1 : Multiuser Python Jupyter Notebooks for Gen AI, ML & DS 11 Lectures 02:08:37
Lecture 1 :
01 - Jupyter AI Python Notebook Course Overview and Objectives Preview
Lecture 2 :
02 - Overview of Generative AI and LLM Capabilities in Jupyter
Lecture 3 :
03 - Setup and installation of Jupyter Python Notebook on AWS Cloud
Lecture 4 :
04 (Part 1) Setup and installation of Jupyter Python Notebook on GCP cloud on CP
Lecture 5 :
04 (Part 2) Setup and installation of Jupyter Python Notebook on GCP cloud on GP
Lecture 6 :
05 (Part 1) Setup of Jupyter Python Notebook on Azure cloud on CPU VM
Lecture 7 :
05 (Part 2) Setup of Jupyter Python Notebook on Azure cloud on GPU VM
Lecture 8 :
06 - Enable Multiuser Environment in Jupyter AI
Lecture 9 :
07 - ChatUI in Jupyter for Generative AI and LLMs
Lecture 10 :
08 - Magic Commands in Jupyter AI
Lecture 11 :
09 - How to Enable HTTPS

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