Data Science & AI
GenAI Fine-Tuning Course
Adapt foundation models to your domain with supervised fine-tuning, LoRA, preference tuning and rigorous evaluation
- Duration
- Live batches
- Format
- Live online
- Modules
- 10 modules
- Certificate
- On completion
- ✓Live online instructor-led training in scheduled batches
- ✓Decide when to prompt, retrieve or fine-tune, with cost and quality evidence
- ✓Dataset design and cleaning for instruction tuning
- ✓Supervised fine-tuning with LoRA / QLoRA on free or low-cost GPUs
- ✓Introduction to preference tuning (DPO) and distillation
- ✓Assure Success course-completion certificate after a fine-tuning capstone
About the course
What this course covers
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
Key skills
Skills you will walk away with
Choosing an adaptation method
Compare prompting, RAG and fine-tuning on quality, cost, latency and maintenance.
Dataset construction
Collect, clean, format and de-duplicate instruction and conversation data.
Supervised fine-tuning
Fine-tune with the Hugging Face stack using chat templates and loss masking.
LoRA and QLoRA
Train adapters on limited GPU memory with quantisation and sensible ranks.
Preference tuning
Apply DPO-style methods using chosen and rejected response pairs.
Distillation
Use a stronger model to produce training data for a smaller, cheaper one.
Curriculum
Module-by-module syllabus
10 modules · 40 topics
1Module 1: When to Fine-Tune4 topics ▾
Learning objective: Choose the right adaptation approach.
- ▸ Prompting, RAG and fine-tuning compared
- ▸ Cost, latency and privacy trade-offs
- ▸ Typical successful use cases
- ▸ Setting success metrics
2Module 2: Open Models and Tooling4 topics ▾
Learning objective: Set up the stack.
- ▸ Open model families and licences
- ▸ Hugging Face Transformers, TRL and PEFT
Schedules
Upcoming live batches
Enroll now and choose your batch dates
Send an enquiry and we'll reserve your seat in the next batch.
Certification
How you get certified
- 1Enrol in a scheduled live batch and confirm GPU or Colab access with the coordinator
- 2Attend sessions and complete the training and evaluation notebooks
- 3Complete the capstone fine-tune with an evaluation report
- 4Meet the attendance and assessment criteria for your batch
- 5Receive your Assure Success course-completion certificate
Career outcomes
Roles this opens up
LLM / GenAI Engineer
₹9–24 LPA (approx.)Adapts and deploys language models for company use cases.
Applied Scientist (NLP)
₹9–24 LPA (approx.)Runs fine-tuning experiments and evaluation.
Machine Learning Engineer
₹8–22 LPA (approx.)Owns training pipelines and serving for adapted models.
AI Consultant (domain adaptation)
₹10–25 LPA (approx.)Advises clients on build, tune or buy decisions for language models.
Salary ranges are approximate figures for India and vary by city, company and experience.
Why this certification is worth it
Decision discipline
You learn when not to fine-tune, which saves time and money.
Efficient methods
Why Assure Success
Learn it once, pass it once
Trainers from the field
Every session is led by a practitioner who uses this material at work.
Batches that fit your week
Weekday and weekend batches in IST, scheduled for working professionals.
Exam-style practice
Practice questions and, where available, a full simulator free with the course.
Exam voucher help
Discounted vouchers and step-by-step help booking your exam.
Human support
WhatsApp support Mon–Sat, 9 AM–8 PM IST — a person answers.
Pay your way
UPI, cards, netbanking and EMI in India; international cards accepted.
FAQs
Questions learners ask us
Q.Do I need expensive GPUs?
No. With LoRA/QLoRA and small open models, exercises run on free or low-cost GPU notebooks. Larger experiments are discussed conceptually.
Q.Is fine-tuning better than RAG?
They solve different problems. RAG supplies fresh facts; fine-tuning changes style, format or skills. Many systems use both. The course shows how to test which one you need.
Q.How much data do I need?
It depends on the task, but quality matters more than quantity. Hundreds to a few thousand well-curated examples can be enough for narrow tasks.
Q.Is there an exam?
No. You get an Assure Success course-completion certificate after your capstone.
Q.Can I fine-tune closed models like those from API vendors?
Some providers offer hosted fine-tuning. We cover the concepts and compare it with open-model tuning, but labs focus on open models.
Q.What background is needed?
Python, PyTorch or Hugging Face familiarity and machine learning basics. Beginners should start with Machine Learning with Python or Generative AI Fundamentals.
Can I use my company's data?
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₹31,999
GenAI Fine-Tuning Course