Master Large Language Models

Program Overview

The LLM Engineering & Deployment Certification is an 8-week, project-based program designed to teach the core skills employers expect from LLM engineers — fine-tuning, evaluation, and deployment using open-source tools.

You'll learn two practical fine-tuning paths: custom training with Hugging Face and Accelerate, and managed workflows using tools like Axolotl and AWS Bedrock. Both approaches are grounded in current practices used in production environments.

The curriculum goes beyond prompting to cover dataset preparation, LoRA/QLoRA, training on single- and multi-GPU setups, model merging, benchmarking with lm-eval, and efficient deployment using vLLM and cloud endpoints.

By the end of the program, you'll complete two portfolio-ready projects — one focused on model tuning, and one on real-world deployment — demonstrating your ability to adapt and serve LLMs for specific use cases.

Who Is This For?

This program is designed for technical learners who want to go deeper into the model layer of LLM-based systems — beyond prompting and orchestration.

Whether you're fine-tuning models for internal tools, domain-specific assistants, or intelligent workflows, this certification equips you with the skills to shape model behavior directly, 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

What You'll Learn

Weeks 1-2: Foundations & Fine-Tuning with Hugging Face

• Learn when to prompt vs. fine-tune vs. use PEFT

• Prepare instruction-tuning datasets using formats like Alpaca

• Train LLMs with Hugging Face Transformers, PEFT, and Accelerate

• Understand tokenization, padding, and assistant-only masking

• Dive deep into LoRA and QLoRA: how they work and when to use them

Week 3: Scaling & Training Optimization

• Fine-tune LLMs with LoRA/QLoRA on single and multi-GPU setups

• Use DeepSpeed ZeRO and Hugging Face Accelerate

• Monitor loss curves, run qualitative checks, and apply early stopping

Week 4: Capstone Project 1 — Fine-Tuning

• Choose a use case (e.g., chatbot, QA, summarizer)

• Prepare a dataset and fine-tune a model end-to-end

• Document results with loss curves and qualitative checks

Week 5: Fine-Tuning with Services & Frameworks

• Fine-tune LLMs using Axolotl, AWS Bedrock, and SageMaker

• Compare cloud vs. local workflows and understand trade-offs

• Build reproducible fine-tuning pipelines with YAML-based tools

Week 6: Evaluation & Model Optimization

• Use lm-evaluation-harness for benchmark testing

• Run bias, robustness, and hallucination checks

• Merge models with MergeKit; quantize with bitsandbytes and GGUF

Week 7: Deployment & Inference

• Deploy models using vLLM, Hugging Face pipelines, or cloud APIs

• Compare local vs. cloud deployment: cost, latency, scalability

• Set up APIs with optimized runtime environments

Week 8: Capstone Project 2 — Deployment

• Deploy your fine-tuned model using at least two methods

• Run tests on latency, cost, and reliability

• Deliver a live demo or hosted API, plus deployment documentation

Certification Access & Enrollment

This is a paid, self-paced certification program. Once enrollment opens, you'll be able to purchase full access to the program.

Enrollment includes everything you need to complete the certification — from lessons and tools to expert project feedback and final certification review.

Included with Enrollment:

• Full access to all lessons, code templates, video tutorials, and project workflows

• Expert review and feedback on both capstone projects

• Official Certificate of Completion

• Project badge on your Ready Tensor profile

• Email support throughout the program

How Certification Works

To earn the LLM Engineering & Deployment Certificate, you must:

• Complete two capstone projects: one on fine-tuning, and one on deployment and inference

• Submit your work individually or as part of a team (up to 3 people)

• Score at least 70% based on the official evaluation rubric

• Publish both projects on Ready Tensor with documentation and a working demo or API

• Certification is awarded after your submissions are reviewed and approved

Program Prerequisites

This is an advanced, technical certification program. You should be confident writing Python and comfortable working with LLM libraries, APIs, and basic ML workflows.

Required Skills:

• Intermediate Python (functions, classes, CLI usage)

• Familiarity with LLM APIs and foundational NLP concepts

• Experience using Hugging Face, PyTorch, or similar frameworks

Recommended Prerequisite: Agentic AI Certified Developer (AAIDC)

• Offers strong preparation in LLM system design and agentic workflows

• Not mandatory, but highly recommended for context and continuity