Build local LLM applications using Python and Ollama from Udemy

If you are a developer, data scientist, or AI enthusiast who wants to build and run large language models (LLMs) locally on your system, this course is for you. Do you want to harness the power of LLMs without sending your data to the cloud? Are you looking for secure, private solutions that leverage powerful tools like Python, Ollama, and LangChain? This course will show you how to build secure and fully functional LLM applications right on your own machine.

In this course, you will:

Set up Ollama and download the Llama LLM model for local use.
Customize models and save modified versions using command-line tools.
Develop Python-based LLM applications with Ollama for total control over your models.
Use Ollama's Rest API to integrate models into your applications.
Leverage LangChain to build Retrieval-Augmented Generation (RAG) systems for efficient document processing.
Create end-to-end LLM applications that answer user questions with precision using the power of LangChain and Ollama.

Why build local LLM applications? For one, local applications ensure complete data privacy—your data never leaves your system. Additionally, the flexibility and customization of running models locally means you are in total control, without the need for cloud dependencies.

Throughout the course, you’ll build, customize, and deploy models using Python, and implement key features like prompt engineering, retrieval techniques, and model integration—all within the comfort of your local setup.

What sets this course apart is its focus on privacy, control, and hands-on experience using cutting-edge tools like Ollama and LangChain. By the end, you’ll have a fully functioning LLM application and the skills to build secure AI systems on your own.

Ready to build your own private LLM applications? Enroll now and get started.

What's inside

Learning objectives

Download and install ollama for running llm models on your local machine
Set up and configure the llama llm model for local use
Customize llm models using command-line options to meet specific application needs
Save and deploy modified versions of llm models in your local environment
Develop python-based applications that interact with ollama models securely

Call and integrate models via ollama’s rest api for seamless interaction with external systems
Explore openai compatibility within ollama to extend the functionality of your models
Build a retrieval-augmented generation (rag) system to process and query large documents efficiently
Create fully functional llm applications using langchain, ollama, and tools like agents and retrieval systems to answer user queries

Download and install ollama for running llm models on your local machine
Set up and configure the llama llm model for local use
Customize llm models using command-line options to meet specific application needs
Save and deploy modified versions of llm models in your local environment
Develop python-based applications that interact with ollama models securely
Call and integrate models via ollama’s rest api for seamless interaction with external systems
Explore openai compatibility within ollama to extend the functionality of your models
Build a retrieval-augmented generation (rag) system to process and query large documents efficiently
Create fully functional llm applications using langchain, ollama, and tools like agents and retrieval systems to answer user queries

Syllabus

Getting started with local models

Introduction

Downloading and Installing Ollama

Setting up Ollama and downloading Llama LLM model

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on building and running large language models locally, which ensures complete data privacy and control without cloud dependencies

Uses Python, Ollama, and LangChain, which are cutting-edge tools for building secure and functional LLM applications

Covers Retrieval-Augmented Generation (RAG) systems, which are essential for efficient document processing and answering user questions with precision

Requires installing and setting up Python, which may be an additional step for some learners who are not already familiar with the language

Employs Llama3.1, which may be updated in the near future, requiring learners to adapt to newer versions and potentially rewrite code

Teaches Ollama's Rest API, which allows learners to integrate models into their applications and extend their functionality

Reviews summary

Build local llm apps practically

According to students, this course provides a highly relevant topic focused on building local LLM applications for privacy and control. Learners appreciate the practical, hands-on approach, especially the guidance on constructing a Retrieval-Augmented Generation (RAG) system using key tools like Python, Ollama, and LangChain. Many find it a good introduction to these technologies and feel they gain valuable skills for real-world applications. However, some note that the initial setup can be challenging and the course is best suited for those with some existing Python or technical background. While providing a solid foundation, a few reviewers suggest it could offer more depth on advanced topics or troubleshooting.

Solid introduction but could go deeper.

"A good starting point, but doesn't delve deeply into optimization or advanced use cases."

"The course covers the basics well, but I was hoping for more advanced LangChain patterns."

"Useful as an overview, but might need supplementary material for complex problems."

"It provides a solid foundation for building simple applications."

Best for learners with prior technical skills.

"This course is not for absolute beginners in programming."

"Helps to have some existing Python knowledge before diving in."

"Assumes a certain level of technical familiarity with development environments."

" Learners with a developer background will find the pace comfortable."

Teaches current, important tools like Ollama & LangChain.

"Great to learn about Ollama for running local models securely."

"The LangChain section was very useful and well-explained."

"Covers the essential tools and concepts needed for local LLM development right now."

"Relevant topic and uses cutting-edge libraries."

Hands-on projects, especially RAG, are valuable.

"The hands-on coding and projects are the strongest part of the course for me."

"I built my first RAG system locally thanks to this course's step-by-step guide."

"The project that ties everything together is practical and rewarding."

"Loved building the end-to-end application; it really solidified my learning."

Initial environment setup can be difficult.

"Had some trouble getting Ollama set up on my specific machine."

"The setup process wasn't as smooth as expected; required some troubleshooting."

"Recommended if you already have some experience setting up Python environments and libraries."

"Spent a bit of time resolving installation issues before starting the actual lessons."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Build local LLM applications using Python and Ollama with these activities:

Review Python Fundamentals

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Reinforce your understanding of Python syntax, data structures, and control flow before diving into LLM application development.

Browse courses on Python

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Review Python syntax and data types.
Practice writing basic Python functions.
Work through introductory Python tutorials.

Review REST API Concepts

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Familiarize yourself with REST API concepts to effectively interact with Ollama's API endpoints.

Browse courses on Web APIs

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Study the basics of RESTful architecture.
Learn about HTTP methods (GET, POST, PUT, DELETE).
Explore API documentation and examples.

Read 'Natural Language Processing with Python'

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Gain a deeper understanding of NLP concepts to enhance your LLM application development skills.

View Natural Language Processing with Python:... on Amazon

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Read the introductory chapters on NLP fundamentals.
Explore the sections on text processing and analysis.
Experiment with the NLTK library for NLP tasks.

Four other activities

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Follow LangChain Tutorials

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Enhance your LangChain skills by working through official tutorials and example projects.

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Explore the LangChain documentation website.
Work through tutorials on prompt engineering.
Implement a basic RAG application using LangChain.

Build a Simple Question Answering App

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Solidify your understanding by building a question answering application using Ollama, LangChain, and a local LLM.

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Set up Ollama and download a suitable LLM.
Implement a RAG pipeline using LangChain.
Create a user interface for querying the application.

Contribute to Ollama Documentation

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Deepen your understanding of Ollama by contributing to its open-source documentation.

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Explore the Ollama GitHub repository.
Identify areas in the documentation that need improvement.
Submit a pull request with your documentation changes.

Write a Blog Post on Local LLMs

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Share your knowledge by writing a blog post about building local LLM applications with Python and Ollama.

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Choose a specific topic related to local LLMs.
Research and gather information for your blog post.
Write, edit, and publish your blog post.

Career center

Learners who complete Build local LLM applications using Python and Ollama will develop knowledge and skills that may be useful to these careers:

Artificial Intelligence Developer

An Artificial Intelligence Developer designs and implements AI solutions, and this course provides strong support for that ambition. You will learn to build and deploy large language models locally, which is frequently required for AI development. By focusing on tools like Ollama, Python, and LangChain, this course prepares you to handle various AI development tasks, including creating retrieval augmented generation systems. Developing Python based LLM applications and using Ollama’s Rest API are skills directly transferable to the role of Artificial Intelligence Developer.

See salaries and explore the career path for Artificial Intelligence Developer

AI Research Engineer

An AI Research Engineer designs and conducts experiments on AI models and algorithms. This course is a natural fit for any AI Research Engineer interested in developing and experimenting with large language models locally. By learning how to build, customize, and deploy models using Python, Ollama, and LangChain, this course provides tools for applied researchers in a private environment that they control completely. Creating retrieval-augmented generation systems specifically enhances capabilities in utilizing large amounts of data.

See salaries and explore the career path for AI Research Engineer

Machine Learning Engineer

A Machine Learning Engineer builds, tests, and deploys machine learning models, and this course is highly relevant to this endeavor. This course focuses on running large language models locally, teaching skills in model customization and deployment, mirroring the work of a Machine Learning Engineer who is responsible for model development and management. The course focuses on tools like Python, Ollama, and LangChain, technologies often used to build and deploy local applications. The hands-on experience in building Retrieval-Augmented Generation systems and creating complete LLM applications directly contributes to skills needed in this engineering role.

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AI Product Manager

An AI Product Manager is responsible for guiding the development and strategy of AI products, and this course provides a strong foundation for this role. The course covers the practical aspects of building local LLM applications, which helps the product manager appreciate the possibilities and limitations of this technology. The ability to understand the capabilities of tools like Ollama, Python, and LangChain lets the product manager make informed decisions about product development. The focus on privacy and local deployment are also especially important for the AI product manager.

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Machine Learning Operations Engineer

A Machine Learning Operations Engineer focuses on deployment, maintenance and scaling of machine learning models, and this course directly addresses key aspects of that role. Focusing on the use of Python, Ollama, and Langchain, the course develops the ability to both deploy and customize language models in a controlled environment. The course's emphasis on building tools and integrating them through the Rest API is also central to the work of a Machine Learning Operations Engineer, making this course a great fit.

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Natural Language Processing Engineer

A Natural Language Processing Engineer focuses on building systems that enable computers to understand and process human language. This course may be useful for a Natural Language Processing Engineer because it covers essential aspects of local LLM development. You will learn core skills in building LLM applications using Python and LangChain, and will gain experience in customizing models. The core focus on local deployment makes this course a potential resource for any engineer focused on applications that must remain private or air-gapped.

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Prompt Engineer

A Prompt Engineer specializes in crafting effective prompts for large language models, and this course may be useful for that purpose. The course provides a solid grounding in LLM interaction, which is a core skill for any prompt engineer. By learning how to build and run language models locally through Ollama, the learner gains hands-on experience in how LLMs respond to different inputs. This hands-on experience, including the development of retrieval augmented generation systems, develops a deeper understanding of how LLMs function.

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Data Scientist

A Data Scientist uses data analysis and machine learning to gain insights and build predictive models. This course may be useful for a Data Scientist interested in exploring or implementing local large language models into their analytics toolkit. By learning how to run models locally using tools such as Ollama and Python, a data scientist can gain a deeper understanding of LLM behavior. The course also provides skills for building retrieval augmented generation systems, expanding a data scientist's ability to process unstructured text data.

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Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course may be useful for a Software Engineer who seeks to incorporate local large language models into their applications. The focus on using Python, Ollama and Langchain to create LLM functionality is directly applicable to software development projects. Creating end-to-end LLM applications and using the Ollama Rest API are core functions of the software engineer who desires to incorporate LLM based tools into their products.

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Computational Linguist

A Computational Linguist uses computers to analyze and process human language, and this course may be useful for those doing projects that must be kept local. This course teaches tools like Python, LangChain and Ollama that are used to build language processing applications. The course provides valuable hands-on experience that is applicable to computational linguistics, while the focus on local deployment gives the learner capabilities that are especially valuable for projects where data must remain private or in an air-gapped setting.

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Data Engineer

A Data Engineer builds and maintains the infrastructure for data storage and processing, and this course may be useful for data engineers who are working with AI tools. The course covers the practical application of large language models and their local deployment through tools like Python and Ollama. The ability to build retrieval augmented systems may provide a data engineer with unique and applicable opportunities in their work. Likewise, the ability to integrate local models into other tools via a Rest API also aligns well with the work of a Data Engineer.

See salaries and explore the career path for Data Engineer

Research Scientist

A Research Scientist conducts research to discover new knowledge or improve existing products and processes. This course may be useful for a Research Scientist interested in large language models, especially those who need to run experiments locally for privacy or customization reasons. The course specifically focuses on the practical application of language models in a local environment, allowing a researcher to test hypotheses and build sophisticated applications without cloud dependencies. Skills gained building retrieval augmented generation systems are useful for enhancing research capabilities by improving processing of unstructured data.

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Solutions Architect

A Solutions Architect designs and oversees the implementation of technology solutions for an organization, and this course may be useful for an architect interested in incorporating local LLM technology. The course will develop skills necessary to evaluate and integrate LLMs in a local environment, as well as understand how they can be incorporated into a wider system. The ability to create end-to-end LLM applications and integrate them through the Rest API are core skills that the solutions architect should possess.

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Robotics Engineer

A Robotics Engineer designs, develops, and tests robots and robotic systems. This course may be useful for a Robotics Engineer seeking to incorporate local large language models into their projects for enhanced interaction or data processing. The ability to build local LLM applications with Python and Ollama may enable the robotics engineer to create sophisticated functionalities in a self contained environment. This could include enhanced interfaces between human operators, or local processing of environmental data for more intelligent behavior.

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Data Analyst

A Data Analyst interprets data and provides insights to drive decision making. This course may be useful for a Data Analyst who wishes to explore the application of large language models in data analysis workflows. Skills developed in this course, such as the ability to build retrieval augmented generation systems which can process unstructured data, may prove useful. Gaining a hands-on understanding of how local LLMs function will give a data analyst a deeper appreciation of the possibilities.

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Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Build local LLM applications using Python and Ollama.

Natural Language Processing with Python

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Provides a comprehensive introduction to NLP using Python and the NLTK library. While the course focuses on Ollama and LangChain, understanding the fundamentals of NLP will greatly enhance your ability to build effective LLM applications. This book is particularly useful for understanding text processing techniques and basic NLP concepts. It serves as a valuable reference for those new to the field.

Natural Language Processing with Python: Analyzing...

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Build local LLM applications using Python and Ollama

What's inside

Learning objectives

Syllabus

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Reviews summary

Build local llm apps practically

Activities

Career center

Reading list

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