AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally

Overview

Build AI-powered applications locally using Qwen 2.5 & Ollama. Learn Python, FastAPI, and real-world AI development (AI) What you'll learn:

Set up and run Qwen 2.5 on a local machine using OllamaUnderstand how large language models (LLMs) workBuild AI-powered applications using Python and FastAPICreate REST APIs to interact with AI models locallyIntegrate AI models into web apps using React.jsOptimize and fine-tune AI models for better performanceImplement local AI solutions without cloud dependenciesUse Ollama CLI and Python SDK to manage AI modelsDeploy AI applications locally and on cloud platformsExplore real-world AI use cases beyond chatbots Are you ready to build AI-powered applications locally without relying on cloud-based APIs?

This hands-on course will teach you how to develop, optimize, and deploy AI applications using Qwen 2.5 and Ollama, two powerful tools for running large language models (LLMs) on your local machine.With the rise of open-source AI models, developers now have the opportunity to create intelligent applications that process text, generate content, and automate tasks—all while keeping data private and secure. In this course, you’ll learn how to install, configure, and integrate Qwen 2.5 with Ollama, build FastAPI-based AI backends, and develop real-world AI solutions.Why Learn Qwen 2.5 and Ollama?Qwen 2.5 is a powerful large language model (LLM) developed by Alibaba Cloud, optimized for natural language processing (NLP), text generation, reasoning, and code assistance.

Unlike traditional cloud-based models like GPT-4, Qwen 2.5 can run locally, making it ideal for privacy-sensitive AI applications.Ollama is an AI model management tool that allows developers to run and deploy LLMs locally with high efficiency and low latency. With Ollama, you can pull models, run them in your applications, and fine-tune them for specific tasks—all without the need for expensive cloud resources.This course is practical and hands-on, designed to help you apply AI in real-world projects.

Whether you want to build AI-powered chat interfaces, document summarizers, code assistants, or intelligent automation tools, this course will equip you with the necessary skills.Why Take This Course?- Hands-on AI development with real-world projects- No reliance on cloud APIs—keep your AI applications private & secure- Future-proof skills for working with open-source LLMs- Fast, efficient AI deployment with Ollama’s local executionBy the end of this course, you'll have AI-powered applications running on your machine, a deep understanding of LLMs, and the skills to develop future AI solutions. Are you ready to start building?

Syllabus

• Introduction to AI Development with Qwen 2.5 & Ollama
Overview of Qwen 2.5 and Ollama
Course objectives and learning outcomes
• Setting Up Your Local Environment
Installing Qwen 2.5 locally
Configuring Ollama for model management
Introduction to Python and necessary libraries
• Understanding Large Language Models (LLMs)
Fundamentals of LLMs
Qwen 2.5’s capabilities and architecture
Comparing local vs. cloud-based LLMs
• Building AI-Powered Applications with Python
Basic Python programming for AI
Introduction to FastAPI for building backends
Creating REST APIs to interact with AI models
• Developing AI Backends with FastAPI
Setting up FastAPI
Integrating Qwen 2.5 with FastAPI
Developing and deploying AI-powered APIs
• Frontend Integration with React.js
Introduction to React.js
Combining React.js with FastAPI for full-stack AI apps
Building web interfaces to interact with AI models
• Optimizing and Fine-Tuning AI Models
Techniques for model optimization
Fine-tuning methods for specific tasks
Evaluating model performance and improvements
• Managing AI Models Locally with Ollama
Using the Ollama CLI
Exploring the Ollama Python SDK
Model lifecycle management and version control
• Deploying AI Applications Locally and on Cloud
Deployment strategies for local execution
Transitioning from local to cloud platforms
Best practices for deploying AI applications
• Real-World AI Use Cases Beyond Chatbots
Exploring applications in NLP, text generation, and automation
Case studies: Document summarizers, code assistants
Ethical considerations and privacy in AI applications
• Course Wrap-Up and Future Directions
Recap of key concepts and skills
Exploring future trends in AI and LLMs
Next steps for continued learning and development

Taught by

Dr. Vivian Aranha

Subjects

Computer Science