By completing this course, learners will:

• Run LLMs locally using Ollama CLI and API.

• Manage multiple models and configurations.

• Build applications with Ollama + Python/Node.js integrations.

• Use embeddings and vector search for contextual AI.

• Fine-tune or customize models for specific tasks.

• Deploy AI apps that balance performance, cost, and privacy.

Course Syllabus

Module 1: Introduction to Ollama

• What is Ollama and why use it?

• Ollama vs cloud-based LLM APIs

• Installing Ollama on macOS, Linux, and Windows

Module 2: Running Models Locally

• Downloading and running pre-trained models

• Switching between models (LLaMA, Mistral, etc.)

• Configuring model settings

Module 3: Ollama CLI & API

• Using the Ollama command-line interface

• Exposing Ollama as a local API

• Basic prompt and response workflows

Module 4: Customizing Models

• Fine-tuning basics

• Model configuration files

• Importing and modifying model weights

Module 5: Embeddings & Context

• Generating embeddings with Ollama

• Using embeddings with vector databases (Pinecone, Weaviate, Milvus)

• Context-aware AI workflows

Module 6: Integration with Applications

• Python and Node.js SDKs

• Connecting Ollama to web apps (Next.js, Flask, etc.)

• Automation with scripts and APIs

Module 7: Advanced Use Cases

• Running Ollama in Docker containers

• Using GPUs for faster inference

• Scaling Ollama across multiple machines

Module 8: Real-World Projects

• Local AI chatbot with Ollama

• Document Q&A system with embeddings

• Code assistant powered by Ollama models

Module 9: Deployment & Security

• Private/local AI deployments

• Security best practices for sensitive data

• Balancing performance vs. hardware limits

Module 10: Best Practices & Future Trends

• Staying updated with new model releases

• Open-source LLM ecosystem overview

• Optimizing Ollama for production apps

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in Ollama and local LLM integration. This certificate demonstrates readiness for roles in AI engineering, full-stack development, and applied machine learning.

Ollama skills prepare learners for roles such as:

• AI Engineer (Local AI Applications)

• Full-Stack Developer (AI-integrated apps)

• Machine Learning Engineer

• Research Engineer (LLM fine-tuning)

• DevOps/Infra Engineer (AI deployment)

With rising demand for private, cost-effective AI solutions, Ollama expertise is highly relevant in enterprises, research, and startups.

1. What is Ollama?
   Ollama is an open-source platform for running LLMs locally, enabling private and customizable AI applications.

2. Which models can Ollama run?
   Ollama supports LLaMA, Mistral, and other open-source LLMs.

3. What’s the advantage of Ollama over cloud APIs?
   Ollama allows local, private, and cost-free execution, without dependency on external servers.

4. How does Ollama expose models for use?
   Via CLI commands and a local REST API.

5. Can Ollama be fine-tuned?
   Yes, Ollama allows custom configurations and fine-tuning of models.

6. What are embeddings in Ollama?
   Embeddings are vector representations of text, useful for semantic search, Q&A, and context injection.

7. How does Ollama integrate with apps?
   Through Python, Node.js SDKs, and API endpoints.

8. Does Ollama require a GPU?
   Not necessarily; it can run on CPU, but GPUs improve inference speed.

9. What are real-world use cases of Ollama?
   Chatbots, document search, code assistants, local Q&A systems, and personal AI apps.

10. How does Ollama compare to LangChain or Semantic Kernel?
    Ollama focuses on running and managing models locally, while LangChain and SK focus on orchestration and chaining of LLM functions.