GenAI Fine-Tuning Course

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GenAI Fine-Tuning Course — Course brochure | Assure Success

About the course

Why take GenAI Fine-Tuning Course?

The GenAI Fine-Tuning Course is for practitioners who need a model that speaks their domain's language, follows a house style or performs a narrow task more cheaply than a large general model. You learn how to decide whether fine-tuning is the right tool, then how to do it properly.

The course covers building high-quality instruction datasets, splitting and decontaminating evaluation data, supervised fine-tuning with parameter-efficient methods such as LoRA and QLoRA, hyperparameter choices, and merging or serving adapters. It also introduces preference optimisation (for example DPO), distilling a large model into a small one, and fine-tuning for structured output or tool calling. Throughout, evaluation is central: you build test sets, compare against prompt-only and RAG baselines, and watch for regressions and overfitting.

There is no external exam. You complete a capstone fine-tune and receive an Assure Success course-completion certificate. A good result requires careful data, not just GPU time, and the course is honest about that.

Who should attend

● ML engineers and data scientists adapting open models for business tasks
● AI engineers who have built RAG or prompt-based systems and hit quality or cost limits
● NLP practitioners moving from classical fine-tuning to LLMs
● Teams with domain data in areas like legal, finance, healthcare or customer support
● Researchers and graduate students working on language model adaptation

Prerequisites

● Solid Python and PyTorch or Hugging Face basics
● Understanding of machine learning evaluation and overfitting
● Familiarity with LLM concepts; Generative AI Fundamentals or NLP with Transformers is a good preparation

What this certification does for you

Decision discipline

You learn when not to fine-tune, which saves time and money.

Efficient methods

LoRA and QLoRA make experiments possible without big GPU clusters.

Evaluation-first

Every experiment is compared with strong baselines.

Portable workflow

The process applies across open model families.

Curriculum

10 modules, step by step

MODULE 1

When to Fine-Tune

Choose the right adaptation approach.

› Prompting, RAG and fine-tuning compared
› Cost, latency and privacy trade-offs
› Typical successful use cases
› Setting success metrics

MODULE 2

Open Models and Tooling

Set up the stack.

› Open model families and licences
› Hugging Face Transformers, TRL and PEFT
› GPU memory basics
› Experiment tracking

MODULE 3

Dataset Design

Build training data that works.

› Instruction and chat formats
› Cleaning, de-duplication and quality filters
› Synthetic data and its risks
› Train, validation and test splits; contamination checks

MODULE 4

Supervised Fine-Tuning

Run a first fine-tune.

› Chat templates and loss masking
› Learning rate, epochs and batch size
› Training diagnostics
› Overfitting and catastrophic forgetting

MODULE 5

Parameter-Efficient Methods

Train within limited hardware.

› LoRA rank, alpha and target modules
› QLoRA and quantisation
› Merging and swapping adapters
› Full fine-tuning compared

MODULE 6

Preference Optimisation and Distillation

Shape behaviour beyond SFT.

› Preference data
› DPO and related methods
› Distilling from a stronger model
› Fine-tuning for JSON and tool calling

MODULE 7

Evaluation

Measure what matters.

› Task metrics and rubric-based grading
› LLM-as-judge strengths and weaknesses
› Human review sampling
› Regression suites

MODULE 8

Serving and Operations

Deploy tuned models.

› Inference engines and batching
› Quantised serving
› Versioning and rollback
› Monitoring quality drift

MODULE 9

Safety, Licences and Governance

Stay responsible.

› Data rights and privacy in training data
› Model licence terms
› Safety evaluation after tuning
› Documentation and model cards

MODULE 10

Capstone Project

Deliver a fine-tuned model with evidence.

› Pick a domain task
› Build dataset and baselines
› Fine-tune and evaluate
› Present results and limitations

What you get

Why take GenAI Fine-Tuning Course?

Who should attend

Prerequisites

What this certification does for you

10 modules, step by step

Certified by Assure Success

Your path to certification

Skills you will gain

Where this course can take you

Why learn with Assure Success