
Learn to deploy llama 3.2 locally, building chat, vision and multimodal apps, and AI agents that run on your machine, cutting costs and preserving data.
Explore what Ollama is and how to install it. Use the Python package to access lemma 3.2 vision and lemma two for mobile, and begin building AI agents and games.
Learn to set up your Python development environment by installing Python, Visual Studio Code, and Jupyter Notebook, with macOS and Windows commands and guidance on Python path.
Explore ollama, an open source framework that runs large language models locally, including llama 3.2, with embeddings and a local vector database to build private AI and reduce cloud costs.
Examine open source LLMs, their code access and fine-tuning advantages, compare with closed source models, and review examples like Llama 3.1/3.2 and Gemma.
Install Ollama on Mac, download and run local models, and manage models via terminal; explore Gemma and Llama 3.2 vision, and use Python library and API for local AI agents.
Set up a VS Code workflow for the Ollama Python library, create and activate a virtual environment, and run a simple llama chat bot with llama 3.2 vision.
Demonstrate a local llama 3.2 vision guessing game, uploading images, converting to base64, and validating challenges like red, landscape, text, and food with a vision response.
Learn to run Ollama as a local API for downloaded models across languages, enabling apps and chrome extensions to generate responses via curl and postman.
Run Ollama on Android to host open source large language models like Deep Sea Carbon using tmux. Install required tools and serve a local API endpoint on mobile.
Explore building ai agents with Ollama by defining an agent, its brain, and a crew to coordinate tasks like math problem solving, using Gemma 2b model.
Build a rack-based chatbot with Llama, embeddings, and a vector database using Lang chain, Gemma 2, and nomic embeddings; learn website scraping, chunking, and retrieval QA for accurate answers.
Celebrate completing the course and join the top 20% by applying what you learned, then deploy Ollama models on cloud in production and explore error logging, upscaling, and downscaling.
Explore AI agents and rag with an end-to-end look at each component, plus how to run them locally and in production, and use the linked courses for deeper learning.
Mastering Ollama: Build Production-Ready AI Applications with Local LLMs
Transform your AI development skills with this comprehensive, hands-on course on Ollama - your gateway to running powerful language models locally. In this practical course, you'll learn everything from basic setup to building advanced AI applications, with 95% of the content focused on real-world implementation.
Why This Course?
The AI landscape is rapidly evolving, and the ability to run language models locally has become crucial for developers and organizations. Ollama makes this possible, and this course will show you exactly how to leverage its full potential.
What Makes This Course Different?
✓ 95% Hands-on Learning: Less theory, more practice
✓ Real-world Projects: Build actual applications you can use
✓ Latest Models: Work with cutting-edge LLMs like Llama 3.2, Gemma 2, and more
✓ Production-Ready Code: Learn best practices for deployment
✓ Complete AI Stack: From basic chat to advanced RAG systems
Course Journey
Section 1: Foundations of Local LLMs
Start your journey by understanding why local LLMs matter. You'll learn:
What makes Ollama unique in the LLM landscape
How to install and configure Ollama on any operating system
Basic operations and model management
Your first interaction with local language models
Section 2: Building with Python
Get hands-on with the Ollama Python library:
Complete Python API walkthrough
Building conversational interfaces
Handling streaming responses
Error management and best practices
Practical exercises with real-world applications
Section 3: Advanced Vision Applications
Create exciting visual AI applications:
Working with Llama 2 Vision models
Building an interactive vision-based game
Image analysis and generation
Multi-modal applications
Performance optimization techniques
Section 4: RAG Systems & Knowledge Bases
Implement production-grade RAG systems:
Setting up Nomic embeddings
Vector database integration
Working with Gemma 2 model
Query optimization
Context window management
Real-time document processing
Section 5: AI Agents & Automation
Build intelligent agents using state-of-the-art models:
Architecting AI agents with Gemma 2
Task planning and execution
Memory management
Tool integration
Multi-agent systems
Practical automation examples
Practical Projects You'll Build
Interactive Chat Application
Build a real-time chat interface
Implement context management
Handle streaming responses
Deploy as a web application
Vision-Based Game
Create an interactive game using Llama 2 Vision
Implement real-time image processing
Build engaging user interfaces
Optimize performance
Enterprise RAG System
Develop a complete document processing system
Implement efficient vector search
Create intelligent query processing
Build a production-ready API
Intelligent AI Agent
Build an autonomous agent using Gemma 2
Implement task planning and execution
Create tool integration framework
Deploy for real-world automation
What You'll Learn
By the end of this course, you'll be able to:
Set up and optimize Ollama for production use
Build complex applications using various LLM models
Implement vision-based AI solutions
Create production-grade RAG systems
Develop intelligent AI agents
Deploy and scale your AI applications
Who Should Take This Course?
This course is perfect for:
Software developers wanting to integrate AI capabilities
ML engineers moving to local LLM deployments
Technical leaders evaluating AI infrastructure
DevOps professionals managing AI systems
Prerequisites
To get the most out of this course, you should have:
Basic Python programming experience
Familiarity with REST APIs
Understanding of command-line operations
Computer with minimum 16GB RAM (32GB recommended)
Why Learn Ollama?
Cost-effective: Run models locally without API costs
Privacy-focused: Keep sensitive data within your infrastructure
Customizable: Modify models for your specific needs
Production-ready: Build scalable, enterprise-grade solutions
Course Format
95% hands-on practical content
Step-by-step project builds
Real-world code examples
Interactive exercises
Production-ready templates
Best practice guidelines
Support and Resources
Complete source code for all projects
Production-ready templates
Troubleshooting guides
Performance optimization tips
Deployment checklists
Community support
Join us on this exciting journey into the world of local AI development. Transform from a regular developer into an AI engineering expert, capable of building and deploying sophisticated AI applications using Ollama.
Start building production-ready AI applications today!