Master Large Language Models

Program Overview

The LLM Engineering & Deployment Certification is a 9-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

Week 1: Foundations of Fine-Tuning

• Understand when to fine-tune vs. prompt vs. use RAG

• Explore frontier vs. open-source LLMs (GPT-4, Claude, LLaMA, Mistral)

• Learn the complete project flow: data → fine-tuning → deployment

• Set up Google Colab for LLM development

• Reproduce Hugging Face leaderboard results

Week 2: Data Prep & Parameter-Efficient Training

• Master tokenization and padding strategies

• Prepare datasets for Hugging Face fine-tuning

• Implement label shifting and assistant-only masking

• Deep dive into LoRA and QLoRA techniques

• Understand parameter-efficient fine-tuning trade-offs

Week 3: Scaling & Advanced Training

• Set up experiment tracking with Weights & Biases

• Fine-tune on Google Colab and RunPod environments

• Scale to multi-GPU training with DeepSpeed ZeRO

• Monitor training metrics and apply optimization strategies

Week 4: Evaluation & Model Optimization

• Benchmark models with lm-evaluation-harness

• Run adversarial, bias, and hallucination detection tests

• Merge models with MergeKit for enhanced performance

• Apply quantization (Bitsandbytes, GGUF) for efficiency

• Use Axolotl for reproducible YAML-based fine-tuning

Week 5: Capstone Project 1 — Fine-Tuning

• Choose and implement a real-world use case

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

• Generate evaluation report and quantized model

• Create model card with complete documentation

Week 6: AWS Foundations for LLM Engineering

• Set up AWS environment (IAM, S3, cost management)

• Fine-tune with SageMaker Training Jobs

• Use AWS Bedrock for managed fine-tuning

• Compare cloud vs. local workflow trade-offs

Week 7: Deployment & Inference Platforms

• Deploy with vLLM for optimized inference

• Use Modal for serverless model hosting

• Set up SageMaker real-time endpoints with auto-scaling

• Compare platforms: cost, latency, and scalability analysis

Week 8: Production Operations & Reliability

• Implement monitoring with CloudWatch and LangSmith

• Set up security, access control, and governance

• Detect and handle model drift in production

• Create comprehensive documentation and handoff procedures

Week 9: Capstone Project 2 — Production Deployment

• Deploy fine-tuned model to multiple platforms

• Conduct latency and cost analysis benchmarks

• Build monitoring dashboard with key metrics

• Deliver live API endpoint with complete 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