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💻   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


💻   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"


💻   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


📱   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

🖥   Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


📚   Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. 👇

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🙂   Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

Also, you are more than welcome to join our community 👉👉 🔗 DeepLearning.AI Forum


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Welcome to Practical Multi AI Agents and Advanced Use Cases with CrewAI. This is a very practical course, meaning you hear about and learn how to build yourself agentic systems that aren't just shiny technology, but they can be deployed and create real value and application settings right now. Multi AI Agent systems involve multiple AI agents working together to achieve complex tasks by collaborating, delegating, and sharing information. If you took the previous multi-AI agent systems course, you learn that multiple agents can be created, each with specific tasks or roles, and these agents can collaborate to perform complex workflows. In this course, you'll learn how to build agent-based apps for even more advanced use cases, and you see numerous real use cases based on what people are actually doing in practice. When you're building multi-agent applications, a key challenge is balancing the speed and quality of results while also maintaining consistency. Different model choices and sizes can impact these factors. In this course, you'll learn to rigorously test your application by measuring key metrics that you can then use to keep on driving further improvements. You'll also learn to train your agents using human feedback to keep on improving your application over time. It's my pleasure to be here with Joao Moura, who is founder of CrewAI and Joao will be your instructor for this course. Joao also taught the previous course on Multi AI Agent Systems, welcome back. Thank you so much. so good to be back here with you. And with this new practical course on multi-agent systems. We're seeing so many companies building multi-agent AI applications, running in aggregate, tens of millions of agents using CrewAI. And creating more complex agent workflows that involve multiple agents working parallel, multiple crews as well, and doing so much rigorous performance testing and training steps. So, Joao, in the previous course you had explained and demonstrated the basic building blocks of multi-agent systems, things like how agents can work together on the tasks, how they can use tools, how you can build some really cool applications with relatively few lines of code. Maybe you can say a bit about what learners can look forward to in this course. Sure, we will start with a quick review of agentic systems. You will then learn how to integrate your multi-agent automation with internal and external systems. This enables your app to perform actions like query internal data, calling existing systems, sending emails, and so much more. Next, you'll learn how to create a variety of crew setups from sequential to parallel and anything in between, including a few hybrid setups. You will learn how to run tasks in parallel, and also how to connect multiple crews in the pipeline. Then we will look at how you can optimize the performance of your multi-agent systems using testing and training methods. After that, you will learn how to create crews that employ multiple different LLM models to complete a task. For example, you could have a researcher agent using a smaller and faster model for a relatively simple task, and the writer agent using a large model that has been fine tuned to reflect your company's brand voice. I will be referring to this as a multi-model approach that lets you mix and match multiple LLM models based on the task at hand, which can help you build a more efficient and customized AI system. Many people have worked to create this course. I'd like to thank the whole CrewAI team and from DeepLearning.AI, Esmaeil Gargari and Geoff Ladwig have also contributed to this course. This course is going to be a lot of fun. You will build several practical apps like an automated project planning, lead scoring and engagement automation support, data analysis and content creation at scale. I've really enjoyed using CrewAI myself and I'm confident that you will too. Let's go on to the next video to get started.
course detail
Next Lesson
Week 1: Practical Multi AI Agents and Advanced Use Cases with crewAI
  • Introduction
    Video
    ・
    4 mins
  • Overview of Multi AI-Agent Systems
    Video
    ・
    12 mins
  • Automated Project: Planning, Estimation, and Allocation
    Video with Code Example
    ・
    14 mins
  • Internal and External Integrations
    Video
    ・
    3 mins
  • Building Project Progress Report
    Video with Code Example
    ・
    12 mins
  • Complex crew Setups
    Video
    ・
    3 mins
  • Agentic Sales Pipeline
    Video with Code Example
    ・
    28 mins
  • Performance Optimization
    Video
    ・
    7 mins
  • Support Data Insight Analysis
    Video with Code Example
    ・
    25 mins
  • Multi-Model Use Cases
    Video
    ・
    3 mins
  • Content Creation at Scale
    Video with Code Example
    ・
    17 mins
  • Agentic Workflows in Industry
    Video
    ・
    10 mins
  • Generate, Deploy and Monitor Crews
    Video
    ・
    8 mins
  • Blog Post Crew in Production
    Video with Code Example
    ・
    6 mins
  • Conclusion
    Video
    ・
    1 min
  • Course Feedback
  • Community