Program Overview
The LLM Fine-Tuning & Deployment Certification is a 12-week, project-based certification program that teaches you to customize, evaluate, and deploy open-source large language models (LLMs) for real-world applications.
You’ll go beyond prompt engineering to learn instruction tuning, parameter-efficient techniques like LoRA, automated and human-in-the-loop evaluation strategies, and modern deployment options including quantization and self-hosted APIs.
By the end of the program, you’ll complete a portfolio-ready LLM project that demonstrates your ability to adapt foundation models to domain-specific tasks and deploy them efficiently.
You will work with open-source instruction-tuned models such as Meta-Llama-3-8B-Instruct or Mistral-7B-Instruct — small, accessible open-source models ideal for fine-tuning.
Who Is This For?
This program is designed for technical learners who want to go deeper into the model layer of LLM-based systems.
Whether you’re fine-tuning models for internal tools, domain-specific assistants, or intelligent workflows, this program equips you with the practical skills to own model behavior — not just call it.
Ideal for:
- AI/ML Engineers and Applied Researchers
- NLP Developers and MLOps Practitioners
- Data Scientists building LLM-enhanced tools
- Agentic AI Developer Certification graduates
⚠️ Note: You should be comfortable working with Python and basic ML tools. Prior LLM or Hugging Face experience is recommended.
What You’ll Learn
Module 1: Foundations of LLM Customization (Weeks 1–4)
- Understand when to prompt, fine-tune, or use PEFT
- Curate instruction datasets and align data formats
- Apply full or parameter-efficient fine-tuning methods (e.g., LoRA)
- Experiment with Hugging Face, PEFT, and Transformers libraries
Module 2: Evaluation and Responsible Adaptation (Weeks 5–8)
- Evaluate LLM outputs using automated and manual metrics
- Analyze hallucination, bias, and robustness
- Track training and testing performance, and prepare model cards
- Refine your tuned model using feedback loops
Module 3: Deployment & Demonstration (Weeks 9–12)
- Quantize and optimize models for efficient inference
- Deploy models as APIs using FastAPI or Gradio
- Compare your tuned model with baseline API models
- Package your project with documentation and reproducibility in mind
Program Prerequisites
This is an advanced, technical certification program. You should be confident writing Python code and comfortable working with LLM libraries and APIs.
Required Skills:
- Intermediate Python programming (functions, classes, CLI usage)
- Familiarity with LLM APIs and basic NLP concepts
- Experience using Hugging Face, PyTorch, or similar frameworks
Recommended Prerequisite: Agentic AI Certified Developer (AACD)
- Provides excellent grounding in system design with LLMs
- Not mandatory, but strongly encouraged for context
Certification Options
Program Access
- Access to lectures, articles, project instructions, and templates is free for all users.
- If you want expert feedback, personalized support, and an official certificate, you can opt into the Certification Track.
Free Plan:
- Access weekly lectures and reading material
- Use project instructions and tools at your own pace
- No expert review or certification issued
Certification Track (Available to Pro/Team Subscribers):
- Expert feedback on your project submission
- Official Certificate of Completion
- Project Badge on your Ready Tensor profile
- Priority support and 1:1 Q&A during your active subscription
How to Join the Certification Track:
- Subscribe to a Pro or Team plan and maintain it through the program
- Competition winners with valid Pro access are automatically eligible
How Certification Works
To earn the LLM Fine-Tuning & Deployment Certificate, you must:
- Complete one full project involving dataset creation, model tuning, evaluation, and deployment
- Submit the project individually or as part of a team (max 3 people)
- Score at least 70% on the evaluation rubric
- Publish your project on Ready Tensor with documentation and a working demo or API
- Final reviews take place in Weeks 13–14 after program completion
