157 minutesFine Tuning LLM Models – Generative AI Course
You will be able to run quantisation, LoRA and QLoRA fine-tunes end to end on hardware most developers already have.
IBM Technology. Tighter focus on the two techniques teams most often confuse. Goes deeper on data freshness, source attribution, and the inference-time speed argument for fine-tuning. Worth watching if you are specifically trying to argue against an unnecessary fine-tune project.
Good for stakeholder conversations because it separates knowledge freshness from model behavior. Still validate current fine-tuning costs, supported models and data-governance constraints before committing.
Explain when retrieval is the right fix and when fine-tuning may actually help.
Basic RAG and fine-tuning vocabulary.
Last reviewed: May 18, 2026
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157 minutesYou will be able to run quantisation, LoRA and QLoRA fine-tunes end to end on hardware most developers already have.
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Hand-picked external courses that go deeper on this topic.
Emory University Goizueta Business School faculty
The deeper, university-level counterpart to our beginner HubSpot marketing pick — Emory's business-school treatment goes past 'how to prompt' into training generative models for brand-specific output, the purchase-funnel economics of AI-generated content, and a full module of genuine skepticism about when generative AI is and isn't worth using in marketing.
IBM AI Academy
The genAI-era answer to the executive-strategy question. Three short courses aimed squarely at business leaders — no technical background required — on where generative AI creates value, how to govern it responsibly, and how to turn a vague "we should use AI" into a concrete, defensible use case. Rated 4.6 across ~700 reviews. Best taken before your next AI budget decision.