Data Science · Advanced
LLM Fine-tuning & Deployment
When and how to fine-tune: LoRA/QLoRA, preference tuning, evaluation and cost-effective serving — with GPU labs included.
A precise, engineering-first treatment of model customisation: when fine-tuning beats prompting and RAG (and when it doesn't), parameter-efficient methods, preference optimisation, rigorous evaluation and serving economics.
Who it's for
ML engineers with Python and one shipped model.
Tools & stack
What you'll walk away with
- Decide fine-tune vs prompt vs RAG from evidence
- Run LoRA/QLoRA and preference-tuning jobs properly
- Evaluate tuned models against honest baselines
- Serve efficiently: quantisation, batching, routing
Curriculum
01Decision & data
- The customisation ladder
- Dataset curation
- Contamination control
- Baseline discipline
02Tuning methods
- LoRA/QLoRA hands-on
- Preference optimisation
- Curriculum strategies
- GPU lab
03Eval & serving
- Eval harness design
- Quantisation trade-offs
- Serving stacks
- Capstone
Pricing
Value-based
Two-part: a one-time enablement fee plus a per-seat rate that falls as the cohort grows.
Indicative rate card · ≈ ₹7,999/hr · $200/hr
Seats limited
Choose a cohort
New cohort dates announced weekly — enrol now and pick your dates with an advisor.
Pay by PO · nothing upfront · net 30 days · GeM & tender-ready
Or reserve a seat online
₹7,999deposit
Reserves your seat & dates. Balance invoiced per your engagement.
Secure · Razorpay · full refund 7+ days before start
Every enrolment includes
- • Qube copilot access throughout
- • All session recordings & materials
- • Graded applied project with review
- • Verifiable certificate on completion
- • 30-day post-programme support
Related programs
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