Advanced, hands-on LLM engineering
A rigorous, project-based program for engineers working with large language models. Build real-world skills — from fine-tuning and evaluation to deploying optimized LLMs using open-source tools and production workflows.
Production-ready skills for fine-tuning, optimizing and deploying LLMs
Watch real instructional videos from the LLM Engg. and Deployment Program
From Unit 2 of the program, this video explains how floating-point data types affect memory, precision, and performance. You'll see how FP32, FP16, and BF16 behave in practice.
These concepts are foundational for understanding LoRA, QLoRA, and modern parameter-efficient fine-tuning workflows.
This program was developed after analyzing hundreds of job postings for AI and LLM engineering roles. We identified the most in-demand skills - from fine-tuning with Hugging Face and LoRA to real-world deployment and evaluation techniques - and built a practical, project-based curriculum to match. Whether you're upskilling or transitioning roles, you'll learn what teams are hiring for today.
Build portfolio-grade projects, and stand out to employers
This program is open for enrollment. Join today for free.
This is a self-paced, project-based program focused on real-world LLM engineering workflows.
You'll apply what you learn by building and shipping two practical projects that mirror how LLMs are developed and deployed in industry.
The goal is simple: gain confidence working with large language models by fine-tuning, evaluating, and deploying them yourself.
This is a self-paced, project-driven program focused on real LLM engineering workflows.
You'll apply what you learn by building and shipping two practical projects.
A two-module journey covering the full LLM engineering lifecycle
Industry-grade LLM fine-tuning projects built as part of the program
Qwen2.5-1.5B Fine-tuning Project
Efficient Recipe Generator
A domain-adapted language model fine-tuned with QLoRA to generate high-quality recipes.
Qwen2.5-1.5B-Instruct Fine-Tuning
Text-to-SQL Generator
A Qwen-based language model fine-tuned with QLoRA to generate accurate SQL from natural language.
Qwen 2.5 0.5B Instruct Fine-Tuning
Nigerian News Headline Generation
A lightweight language model fine-tuned with QLoRA to generate culturally relevant Nigerian news headlines.
QLoRA Fine-Tuning Project
Article Summarization
A Qwen-based language model fine-tuned with QLoRA to generate concise summaries of scientific articles.
Build your own with the LLM Engineering and Deployment Program
This program is open for enrollment. Join today for free.
Learn from an Industry Leader Working at the Cutting Edge of Generative AI
This program isn't theory-first or trend-driven. It's built from real hiring data and hands-on experience fine-tuning and deploying LLMs in production. My goal was to create the kind of training I wish I had when starting out — practical, focused, and career-relevant.Abhyuday Desai, Ph.D.Founder & CEO of Ready Tensor, Lead Instructor for this Program
Answers to common questions about the LLM Engineering & Deployment Program.
This program is designed software developers, AI/ML engineers, applied researchers, NLP developers, MLOps practitioners, and data scientists who want hands-on experience with fine-tuning, evaluation, and enterprise-grade deployment of large language models.
This program assumes prior experience with Python, PyTorch, and core machine learning workflows. If you're new to neural network training, especially using PyTorch, we recommend completing a foundational program to build the necessary background before enrolling.
You should be comfortable with intermediate Python programming, familiar with Hugging Face Transformers package and PyTorch, and have experience using LLM APIs.
Most learners complete the program in 5-8 weeks. The exact timeline depends on your prior experience and how much time you dedicate each week.
The program consists of 10 units. Each unit typically takes between 5-10 hours to complete, depending on the topic and your prior experience. Some units are lighter and more conceptual, while others are more hands-on and time-intensive.
The program is fully self-paced. Project submissions are reviewed on a regular schedule, but you can submit your work whenever you're ready.
Yes. You'll need access to cloud GPU resources and, in some cases, API keys. The program uses platforms like RunPod and AWS. Most learners spend approximately $10-$20 on cloud resources, and costs may be lower if you have access to local GPUs.
This program prepares you for LLM engineering roles focused on model fine-tuning, evaluation, inference optimization, and deployment. These skills are commonly required for senior AI Engineer, Machine Learning Engineer, and applied LLM engineering roles.
Join hundreds of AI engineers worldwide developing and deploying production-grade LLM systems
This program is open for enrollment. Join today for free.