Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
AI Agents using CrewAI Studio & Jupyter with GPU support
Rating: 5.0 out of 5(1 rating)
373 students
Created byTechLatest Net
Last updated 10/2025
English

What you'll learn

  • Build, orchestrate, and scale autonomous AI agents using CrewAI and CrewAI-Studio
  • Visually design multi-agent AI workflows with no-code tools
  • Write and deploy agent logic programmatically in JupyterHub
  • Optimize and run AI agents on NVIDIA GPU-accelerated virtual machines
  • Debug, test, and iterate AI agent workflows using visual and code interfaces

Course content

3 sections • 10 lectures • 1h 59m total length

  • Course Overview2:35

    In this introductory video, get a clear overview of what this CrewAI training course covers. We’ll walk you through the learning path, key topics, and hands-on demonstrations you can expect — from understanding CrewAI’s architecture to deploying it on cloud platforms.

  • Overview of CrewAI Platform7:07

    Exploring CrewAI: Concepts, Flows, and Best Use Cases

    Learn what makes CrewAI unique in the world of AI orchestration. This video explores core concepts like agents, tasks, crews, tools, and flows. You’ll also understand the differences between Crews and Flows, how they work together, and why CrewAI is a powerful platform for AI-driven automation. We’ll also take a brief tour of the official documentation.


  • Quick Introduction to CrewAI Studio9:22

    Quick Tour of the CrewAI Studio Interface & Features Overview

    In this video, we introduce you to CrewAI Studio, the powerful web-based interface for building, managing, and executing AI agent workflows. You'll get a guided tour of the Studio’s layout, including how to navigate projects, define agents and tasks, configure crews, and monitor executions — all through an intuitive, user-friendly web interface.

  • Quick Introduction to Jupyter11:53

    Tour to Jupyter Notebook, Multi-User environment using Jupyterhub and AI Tools Support

    Discover how to use the Jupyter Notebook environment included with this 'AI Agents using CrewAI Studio & Jupyter with GPU support' virtual machine solution. In this video, we walk you through the basics of using Jupyter in your VM environment. You'll get a tour of JupyterLab, classic Notebooks, and JupyterHub for multi-user access. We’ll also show you how to install additional Python packages and manage your environment effectively

Requirements

  • Basic understanding of AI concepts and large language models (LLMs) is recommended but not mandatory.
  • Familiarity with Python programming will be helpful for using the code-driven features via JupyterHub, but not required for using the no-code CrewAI-Studio interface.
  • No prior experience with CrewAI is needed—the course includes hands on demos and a guided setup to help you get started quickly.
  • A system with NVIDIA GPU support is recommended for optimal performance, but the virtual machine can also run on CPU-only systems.
  • An internet connection and a modern web browser are required for accessing the browser-based tools (CrewAI-Studio and JupyterHub).

Description

Build, orchestrate, and scale autonomous AI agents both visually and programmatically with this course featuring CrewAI, CrewAI-Studio, and JupyterHub—fully optimized for NVIDIA GPU acceleration. Designed for both technical and non-technical users, this course enables rapid development of multi-agent systems powered by leading LLMs like OpenAI, Cohere, Anthropic, and Mistral. CrewAI provides a powerful framework for creating collaborative, role-based agents that can reason, delegate, and complete complex tasks.

With CrewAI-Studio’s intuitive no-code interface, you can visually design agent workflows, define roles, assign tools, and deploy systems without writing a single line of code. Debug, test, and iterate live in your browser and Import/export configurations via YAML for reusability and DevOps integration.

For advanced users, JupyterHub offers a robust coding environment preloaded with popular AI/ML libraries and support for generative AI via Jupyter AI extensions. Making your AI/ML projects more collaborative by providing multi-user environment and enabling easy code and data sharing.

GPU acceleration ensures fast inference, real-time orchestration, and scalable experimentation.

Learn to create intelligent, role-based agents using a no-code interface or dive into code with Jupyter. This supports fast LLM interactions, real-time workflows, and scalable AI projects. Perfect for both beginners and developers looking to create multi-agent systems with ease. Seamlessly hop over from ideas to production ready solutions.

Who this course is for:

  • AI enthusiasts and developers interested in building autonomous, multi-agent systems using LLMs.
  • Data scientists and machine learning engineers looking to explore agent orchestration and workflow automation.
  • Product managers, analysts, and non-developers who want to visually design and test AI agent systems using no-code tools like CrewAI-Studio.
  • Technical teams building or prototyping LLM-powered applications with collaborative agents.
  • Anyone curious about CrewAI, CrewAI-Studio, or JupyterHub and how they can be used to create powerful AI-driven solutions.

Report abuse