Apr 15
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🧪 Customize LLMs & Agents for FREE — https://kode.wiki/3QcX45W Most teams rely on prompt engineering. The ones building reliable production AI agents are fine-tuning their models. This video walks you through the complete data preparation pipeline for fine-tuning LLMs using LoRA and QLoRA, inside a real hands-on KodeKloud lab with a live Secure Ops scenario. No fluff. No theory overload. Just structured, hands-on learning starting from why your training data format matters, all the way to testing your dataset against a live LLM for alignment scoring. ───────────────────────────────────────── 📌 WHAT YOU'LL LEARN IN THIS VIDEO ───────────────────────────────────────── ✅ Why fine-tuning beats prompt engineering for enterprise AI agents ✅ How LoRA and QLoRA work and why they make fine-tuning viable on consumer GPUs ✅ Memory math breakdown: 1B, 7B, and 70B parameter models with QLoRA ✅ How to transform raw security logs into JSONL training data 🧪 FREE HANDS-ON LAB INCLUDED — https://kode.wiki/3QcX45W Practice everything in a real sandbox environment with no local setup, no credit card, no surprises. GPU environment, dependencies, and all lab tasks are already configured and ready to go. ⏱️ TIMESTAMPS 00:00 – Introduction: Why Fine-Tuning Beats Prompt Engineering 00:38 – Hardware Requirements: 01:04 – LoRA and QLoRA Explained 02:10 – Training Data Requirements 03:31 – Lab Intro - Customize LLMs & Agents 04:54 – Task 0: Environment Setup 05:18 – Task 1: Why Data Format Matters 06:14 – Task 2: Log Transformation 07:38 – Task 3: Agent Persona Training Data 08:50 – Task 4: Classification Dataset 09:41 – Task 5: Data Quality Validation 10:33 – Task 6: Verify with LLM Inference 11:38 – Key Takeaways #LLMFineTuning #QLoRA #LoRA #AIAgent #MachineLearning #LargeLanguageModels #DevOps #KodeKloud #AITraining #FineTuneGPT #MLOps #AIEngineer #DataPreparation #HandsOnLab #CloudAI #OpenAI #DeepLearning #GenerativeAI #AIDevOps #LLMTraining #AITutorial #LearnAI #PromptEngineering