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Advanced LLM Application Building

4 Weeks


Cohort-based Course

Learn how to build Production-grade RAG and LLM Applications using AWS and Azure with FAST API. Focus on Scale, Security and Low Latency

Previously at

Stanford University
University of Minnesota

Course overview

Go beyond basic frameworks and deploy production-grade API endpoints and apps

Welcome to the comprehensive course on advancing your skills in building sophisticated Large Language Model (LLM) applications!

If you have already acquired knowledge about RAG, cosine similarity, vector databases, and Langchain, it's time to delve into the practical aspects of packaging and deploying these models in production environments.

This course builds upon the fundamental building blocks of LLMs and covers the following key topics:

1. Fine-tuning: Learn advanced techniques for fine-tuning LLMs (ChatGPT and Open-source LLMs) to enhance their performance and adapt them to specific tasks or domains.

2. Model merging: Explore methods to merge multiple models, optimizing their collective capabilities for more robust and versatile language processing.

3. Inference speed exploration: Understand strategies to optimize and accelerate inference speeds, ensuring efficient real-time processing of language model outputs.

4. Quantization methods: Dive into techniques for model quantization, reducing model size while maintaining performance, crucial for deployment in resource-constrained environments.

5. Model hosting and deployments: Gain insights into best practices for hosting and deploying LLMs in production settings, ensuring seamless integration into diverse applications.

Throughout the course, we will analyze state-of-the-art AI products, reverse-engineering some through Python.

Additionally, my collaboration with experienced Software Engineers on our team will provide valuable insights into integrating LLMs with Node.js for web application development.

As a bonus, you'll have access to experimental products being developed at, my startup, allowing you to stay at the forefront of cutting-edge advancements in the field.

Prerequisites for this course include proficiency in Python and a solid understanding of RAGs, as well as Encoder and Decoder models.

If you feel the need for a more foundational course, consider checking out my other offering on LLMs:

Tools utilized in this course include VS Code, UNIX terminal, Jupyter Notebooks, and Conda package management, ensuring a hands-on and practical learning experience.

This course is for you if you are a:


Machine Learning Engineer exploring different techniques to scale LLM solutions


Researcher, who would like to delve in to various aspects of open-source LLMs


Software Engineer, looking to learn how to integrate AI into their products

What you’ll get out of this course

Advanced Deployment Skills

Gain hands-on experience and mastery in deploying Large Language Models (LLMs) in real-world production environments, covering the entire spectrum from model packaging to seamless integration into diverse applications.

Fine-Tuning Expertise

Acquire advanced techniques for fine-tuning LLMs, enabling you to adapt these models to specific tasks or domains and enhance their performance in targeted applications.

Optimized Model Integration

Learn the art of model merging to combine multiple models effectively, optimizing their collective capabilities for robust and versatile language processing tailored to your application's requirements.

Efficient Inference Processing

Explore strategies for exploring and optimizing inference speeds, ensuring that your language models perform efficiently in real-time scenarios, a crucial skill for deploying responsive and scalable applications.

Cutting-Edge Insights

Dive into the analysis of state-of-the-art AI products, reverse-engineering some through Python, and gain exclusive access to experimental products developed at, staying at the forefront of innovations in the field of advanced language modeling.

This course includes

7 interactive live sessions

Lifetime access to course materials

8 in-depth lessons

Direct access to instructor

2 projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Expand all modules

    Meet your instructor

    Hamza Farooq

    Hamza Farooq

    I am a Founder by day and Professor by night. My work revolves in the realm of LLMs and Multi-Modal Systems.

    My startup, was built with one vision: provide scalable LLM Solutions for Startups and Enterprises, which can seamlessly integrate within the existing ecosystem, while being customizable and cost efficient.

    This course is a cumulation of all my learnings and the courses I teach at other universities

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    Course schedule

    4-6 hours per week
    • Sundays

      9:00 - 11:00am PT

      Virtual Class

    • Weekly projects

      2-3 hours per week

      Work in teams to build solutions, this requires engagement with other team members

    Learning is better with cohorts

    Learning is better with cohorts

    Active hands-on learning

    This course builds on live workshops and hands-on projects

    Interactive and project-based

    You’ll be interacting with other learners through breakout rooms and project teams

    Learn with a cohort of peers

    Join a community of like-minded people who want to learn and grow alongside you

    Frequently Asked Questions

    What happens if I can’t make a live session?
    I work full-time, what is the expected time commitment?
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