Description
Large Language Models (LLMs) are at the forefront of the current revolution in artificial intelligence, unlocking unprecedented opportunities in automation, human-machine interaction, and dynamic information retrieval. In a world where businesses and organizations increasingly demand tailored, efficient, and scalable solutions, the ability to build intelligent agents powered by LLMs is becoming indispensable.
Intelligent agents extend the capabilities of LLMs by providing them with tools to interact with their surrounding environment, making them operational in the real world. This ability transforms LLMs from passive systems that process and generate text into active agents capable of performing real-world tasks, such as retrieving live data, interfacing with APIs, and automating workflows.
Much like previous generations of AI models, these agents can operate in two primary modes:
- Autonomous Operation in the Backstage: Agents can independently perform tasks, manage workflows, and execute actions without human intervention, streamlining complex processes.
- Collaborative Support: Agents can actively assist human users by providing real-time support, facilitating decision-making, and enhancing productivity. This dual functionality allows agents to not only complement human effort but also unlock new possibilities for human-AI collaboration. This is a lot more than a common Chat Bot.
This course goes beyond theoretical knowledge, focusing on practical skills through real-world use cases and advanced tools. Participants will gain the ability to design and implement LLM-based solutions tailored to complex needs, emphasizing innovation, security, and scalability. During the course, Python with Google Colab will be the primary tool for development and experimentation. We will also use Studio LM for running LLMs offline on personal devices, interacting with local documents, and exploring and downloading models from Hugging Face repositories.
Topics include
What you will be able to do
This program aims to equip professionals with the skills to:
- Understand the fundamentals of LLMs, including their architecture and core principles.
- Select the most suitable model for specific use cases, evaluating solutions from Google, OpenAI, IBM, and open-source alternatives.
- Apply advanced techniques, such as Chain of Thoughts and ReActs, to enhance reasoning and generative capabilities.
- Integrate local and cloud-based models, optimizing performance and resources.
- Understand vector databases, essential for retrieving and managing semantic representations.
- Perform fine-tuning of LLMs, customizing them for specialized applications and domains.
- Build custom AI agents using LangChain, a versatile framework that extends LLM capabilities with operational functionalities.
- Implement Retrieval-Augmented Generation (RAG), leveraging reliable sources such as documents, databases, and knowledge graphs to improve response accuracy.
- Overcome traditional model limitations, ensuring access to up-to-date data while reducing inconsistencies and contradictions.
- Protect data privacy, controlling where data are stored and where LLMs are executed.
Duration
4 days or 8 sessions
Prerequisites
Ability to understand and write Python code
Audience
This course is ideal for Data Scientists, AI Developers, and IT Professionals aiming to master Large Language Models (LLMs). It suits those interested in understanding and using the most recent AI solutions (LLMs and Agents) to provide solutions for a wide variety of business problems.