Experimenting with Foundation Models: Accessible Ways to Learn About LLMs and AI

Übersicht

Uncover cost-effective techniques to delve into Foundation Models and Large Language Models (LLMs) using Amazon Bedrock along with Python. Enhance your practical AI skills through insightful demos and tutorials presented by AWS Hero Faye Ellis.

Additionally, seize the opportunity to learn about the in-demand AI skills vital for 2025.

This event is perfect for those eager to expand their knowledge in the field of artificial intelligence and computer science. With its practical approach and hands-on experience, participants can acquire valuable insights into emerging AI trends and technologies.

Lehrplan

• Introduction to Foundation Models
Overview of Foundation Models and Large Language Models (LLMs)
Importance and impact of LLMs in AI
• Introduction to Amazon Bedrock
Overview of Amazon Bedrock services
Benefits of using Amazon Bedrock for AI experimentation
• Setting Up Your Environment
Installing Python and necessary libraries
Accessing and setting up Amazon Web Services (AWS)
Introduction to Jupyter Notebooks for experiments
• Experimenting with Foundation Models using Python
Basic Python skills for AI experimentation
Running pre-trained models using Amazon Bedrock
Interacting with language models: Inputs and outputs
• Hands-on Demos and Tutorials
Tutorial 1: Text Generation with LLMs
Tutorial 2: Sentiment Analysis using Foundation Models
Tutorial 3: Entity Recognition in text
Case Studies of practical AI applications
• Affordable Experimentation Strategies
Cost-effective ways to use AWS for AI
Leveraging open-source tools and resources
• Insights into In-Demand AI Skills for 2025
Key AI skills employers look for
Emerging trends in AI and their implications
• Final Project: Designing and Implementing AI Experiments
Proposal and objectives
Implementation phase with peer reviews
Presentation and feedback session
• Conclusion and Further Resources
Summary of key learning points
Additional resources for continued learning in AI and LLMs

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Fachgebiete

Computer Science