Learn AI with HANDS-ON Workshops
Understanding and Implementing AI Agents (A practical guide to AI processes that reason, plan, and e
In this workshop, get an explanation of what AI agents are, how they work, and a survey of current AI frameworks and how to implement agents in them including LangChain, CrewAI, and Microsoft’s AutoGen. We'll also discuss writing custom tools for agents, multiagent implementations, and using datasets with agents for Agentic RAG.
Understanding MCP (Model Context Protocol) - (Understanding how AI agents can connect to the world)
MCP, or Model Context Protocol, is a standardized framework that allows AI agents to seamlessly connect with external data sources, APIs, and tools. Its main purpose is to make AI agents more intelligent and context-aware by giving them real-time access to live information and actionable capabilities beyond their built-in knowledge.
In this workshop, we'll learn what MCP is, how it works, and how it can be used to create AI agents that can work with any process that implements MCP.
Understanding and Using RAG (Pairing your data with Gen AI)
Integrating AI into your SDLC (Leveraging AI tooling across the phases of your software development
Large language models (LLMs) are great and provide a lot of utility out of the box - as long as you accept that the data they have is only as recent as their training. And, as long as you understand that that they don't have context of any data they weren't trained on - which may be the local context you really care about. In this session, we'll see how to augment their training and knowledge to include other content that they weren't trained on, such as your files. And we'll see how to seamlessly combine that with the LLM's existing knowledge and query/prompt interfaces.
Integrating AI into your SDLC (Leveraging AI tooling across the phases of your software development
Integrating AI into your SDLC (Leveraging AI tooling across the phases of your software development
Integrating AI into your SDLC (Leveraging AI tooling across the phases of your software development
Just as CI/CD and other revolutions in DevOps have changed the landscape of the software development lifecycle (SDLC), so Generative AI is now changing it again. Gen AI has the potential to simplify, clarify, and lessen the cycles required across multiple phases of the SDLC. Join us to look at the multiple ways we can leverage AI to simplify and streamline software development from start to finish. We'll include hands-on exercises to demonstrate key points.
Working with Hugging Face - the "GitHub" for LLMs
Integrating AI into your SDLC (Leveraging AI tooling across the phases of your software development
Working with Hugging Face - the "GitHub" for LLMs
HuggingFace.co is the premier site for all things around large language models (LLMs). It has free and open source models and datasets to easily access, download and start working with. As well, it provides the well-known Transformers library of functions that you can use with Python to code up quick and simple apps that interface with LLMs for chatbots, sentiment analysis, classification, etc.
Join us for an exploration of the site and learn how to navigate it and use its many features. We'll also see how to use its models and the Transformers library with simple Python coding to do typical LLM interactions.
AI for Developers
Integrating AI into your SDLC (Leveraging AI tooling across the phases of your software development
Working with Hugging Face - the "GitHub" for LLMs
Generative AI is everywhere these days. But there are so many parts of it and so much to understand that it can be overwhelming and confusing for anyone not already immersed in it. In this full-day workshop, open-source author, trainer, and technologist Brent Laster will explain the concepts and working of Generative AI from the ground up. You’ll learn about core concepts like neural networks all the way through to working with Large Language Models (LLM), Retrieval Augmented Generation (RAG) and AI Agents. Along the way we’ll explain integrated concepts like embeddings, vector databases and the current ecosystem around LLMs including sites like HuggingFace and frameworks like LangChain. And, for the key concepts, you’ll be doing hands-on labs using Python and a pre-configured environment to internalize the learning.
DIY Gen AI: Running LLMs locally with Ollama, Hugging Face, and LM Studio
DIY Gen AI: Running LLMs locally with Ollama, Hugging Face, and LM Studio
DIY Gen AI: Running LLMs locally with Ollama, Hugging Face, and LM Studio
LM Studio is an easy to use desktop app for experimenting with local and open-source Large Language Models (LLMs) by hosting them on your own system.
Hugging Face is a community hub focused on creating and sharing AI models. It provides many free and pre-trained models as well as datasets and tools to use with them.
Ollama is a command line tool for downloading, exploring, and using LLMs on your local system.
In this hands-on workshop, we'll cover the basics of getting up and running with LM Studio, Ollama and give you hands-on labs where you can use them and Hugging Face to find and load and run LLMs, interact with it via Chat and Python code and more!
GitHub Copilot Complete
DIY Gen AI: Running LLMs locally with Ollama, Hugging Face, and LM Studio
DIY Gen AI: Running LLMs locally with Ollama, Hugging Face, and LM Studio
GitHub Copilot is a generative AI tool for coding that assists developer in writing code more efficiently and faster. This full-day course will help you gain a comprehensive understanding of the tool's capabilities and how to use it effectively in your day-to-day coding.
In this full-day class, we'll cover the basics of Copilot and provide you with hands-on experience through labs. You'll learn the what, why, and how of Copilot and see how to leverage its generative AI functionality in daily coding tasks across multiple languages. You'll also learn key techniques and best practices for working with Copilot.
Buiding GitHub Copilot Extensions (Extend Copilot with your own apps)
DIY Gen AI: Running LLMs locally with Ollama, Hugging Face, and LM Studio
GitHub Copilot Extensions are integrations designed to enhance GitHub Copilot’s functionality by allowing it to interact with external tools, services, and custom workflows directly within development environments. These extensions enable developers to perform tasks such as querying databases, monitoring deployments, retrieving documentation, or automating actions without leaving their coding interface, thereby reducing context switching and improving productivity
In this workshop, we’ll explore how to extend GitHub Copilot’s functionality to work with your own apps through the GitHub Copilot Extensibility Platform. Attendees will learn about writing Copilot Agents and skillsets – the two ways to interact with the platform and see how to use them to create your own custom extension and integrate and share it with others using Copilot.
Automating Testing with GitHub Copilot
Automating Testing with GitHub Copilot
Would you like to learn to use GitHub Copilot to write tests for you? Would you like to learn how to use it to tell you how to test in a language or framework that you're not familiar with? Would you like to have it help you find edge cases to test, or validate inputs? Did you know you can have Copilot generate the tests before the code and then the code to implement Test-Driven Development?
If you're a software engineer, writing tests is part of your job. And, likely not the thing you want to spend most of your time on. Join us in this half-day workshop, as we present material on multiple ways to leverage Copilot for testing your code on any platform and framework.