AI Agents A-Z
Published by Pearson
Master AI agents: frameworks, deployment, evaluation, best practices, and more
- Explore how reasoning models like O1, Claude 3.7, and DeepSeek R1 impact agent reliability
- Learn to utilize a variety of AI agent frameworks, such as CrewAI, LangChain, and AutoGen
- Engage in practical, real-world exercises that guide you through setting up, deploying, and iterating on AI agents
- Gain insights into cost projections and evaluate the pros and cons of open versus closed source options, enabling you to make informed decisions for your projects.
This course provides a comprehensive guide to understanding, implementing, and managing AI agents both at the prototype stage and in production. Attendees will start with foundational concepts and progressively delve into more advanced topics, including various frameworks like CrewAI, LangChain, and AutoGen as well as building agents from scratch using powerful prompt engineering techniques. The course emphasizes practical application, guiding participants through hands-on exercises to implement and deploy AI agents, evaluate their performance, and iterate on their designs. We will go over key aspects like cost projections, open versus closed source options, and best practices are thoroughly covered to equip attendees with the knowledge to make informed decisions in their AI projects.
As AI agents become increasingly integral in various industries for tasks such as customer service, data analysis, and automation, understanding how to effectively build and manage these systems is crucial. This course prepares participants to leverage AI agents to enhance their applications, making them more responsive, efficient, and capable of handling dynamic data environments.
What you’ll learn and how you can apply it
- Implement Diverse AI Agents: Develop the skills to implement AI agents using various frameworks such as CrewAI, LangChain, and AutoGen.
- Deploy AI Agents Effectively: Learn techniques to deploy AI agents in different environments, ensuring seamless integration and functionality.
- Evaluate and Iterate on AI Agents: Master methods to evaluate AI agent performance, enabling continuous improvement and iteration based on real-world feedback.
- Manage Cost and Source Options: Understand how to project costs and make informed decisions about using open versus closed source options for AI agent deployment.
This live event is for you because...
- You seek Practical AI Solutions: Ideal for professionals aiming to apply AI in practical settings, whether for business strategies, customer experiences, or tech innovations.
- You are a Data Scientist: Interested in leveraging real-time data to improve model performance and exploring advanced AI agent frameworks.
- You are an AI Enthusiast: Eager to delve into the latest AI technologies and practical applications of AI agents in various industries.
Prerequisites
- Basic to Intermediate Python Skills: A solid understanding of Python is essential, as it will be the primary programming language used for demonstrating AI agent integration and handling data.
- Foundational Knowledge of AI and Machine Learning Concepts: Familiarity with basic AI and machine learning principles is crucial to grasp the more advanced topics covered in the course.
- Introductory Experience with NLP Models: Having some prior experience with Natural Language Processing (NLP) models will be beneficial, as the course will delve into integrating these models with AI agents for various applications.
Course Set-up
- Python Environment: Ensure that Python is installed on your machine. We recommend using the Anaconda distribution for its ease of use and compatibility with data science libraries.
- GitHub Repository: Access course materials, including code samples and datasets, here. This repository will contain all necessary files for hands-on exercises and example implementations.
- Necessary Libraries: Install the required Python libraries using pip or conda found on Github
- For Non-Developers: You’ll be guided through setting up and using the provided materials, so no prior experience with GitHub is necessary. Support will be available to help with any setup issues.
Recommended Preparation
- Read: Introduction to Transformers for NLP: With the Hugging Face Library and Models to Solve Problems by Shashank Mohan Jain
- Attend: Hands-on NLP with Transformers by Sinan Ozdemir
- Explore: Expert Playlist AI Unveiled by Sinan Ozdemir
Recommended Follow-up
- Read: Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs by Sinan Ozdemir
- Watch: Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) by Sinan Ozdemir
- Read: Quick Start Guide to Large Language Models by Sinan Ozdemir
- Watch: Quick Start Guide to Large Language Models (LLMs) by Sinan Ozdemir
- Watch: Practical Retrieval Augmented Generation (RAG) by Sinan Ozdemir
- Watch: Modern Automated AI Agents by Sinan Ozdemir
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: Introduction to AI Agents (25 minutes)
- Overview of AI agents
- Basic concepts and definitions
- Importance and emerging real-world applications
Segment 2: Frameworks for AI Agents (35 minutes)
- Introduction to CrewAI, LangChain, and AutoGen
- Building Custom Agents from scratch with prompt engineering
- Exercise: Implement a simple AI agent using one or more frameworks
- Q&A (5 mins)
Break (10 minutes)
Segment 3: Deployment Strategies (25 minutes)
- Best practices for deployment
- Open vs. closed source options
- Real-world deployment examples
Segment 4: Evaluation Techniques (20 minutes)
- Methods for evaluating AI agent performance
- Common challenges and solutions
- Key metrics and benchmarks for agent deployments
- Exercise: Implement evaluation metrics
- Q& A (5 minutes)
Break (10 minutes)
Segment 5: Iteration and Improvement (25 minutes)
- Techniques for iterating on AI agent designs
- Continuous improvement strategies
- Case studies of successful agent iterations
- Exercise: Improve our existing AI agents
Segment 6: Cost Projections and Management (20 minutes)
- Understanding costs in AI agent deployment
- Budgeting and financial planning
- Cost-effective deployment strategies
- Q&A (5 minutes)
Segment 7: Advanced Integration Techniques + Best Practices (30 minutes)
- Integrating real-time data into AI agents
- Ensuring data quality and consistency
- Industry best practices for AI agent development
- Ethical considerations and compliance
- Exercise: Integrate real-time data feed
Segment 8: Future Trends and Next Steps (20 minutes)
- Emerging trends in AI agent technology
- Innovations on the horizon
- How to stay updated and continue learning
Course wrap-up and next steps (5 minutes)
- Recap of key concepts
- Final thoughts and recommendations
- Course feedback and evaluation
Your Instructor
Sinan Ozdemir
Sinan Ozdemir is the founder of Crucible, an AI factory platform that helps teams convert existing workflows into custom models. He is a Y Combinator alum, AI & LLM Advisor at Tola Capital, and the author of multiple books on data science and machine learning including Building Agentic AI, Quick Start Guide to LLMs, and Principles of Data Science. Sinan is a former lecturer of data science at Johns Hopkins University and the founder of Kylie.ai, an enterprise-grade conversational AI platform (acquired 2014). He holds a master's degree in pure mathematics from Johns Hopkins University and is based in San Francisco, California.