LLM Fine-Tuning Mastery: Basic to Advanced & Cloud Deploy

Professional LLM Fine-Tuning: LoRA, QLoRA, RLHF -DPO Techniques,Hugging face + Azure, AWS, GCP Cloud Deployment in 2025

What you’ll learn
Use LoRA and QLoRA adapters to fine-tune BERT, GPT, Llama, Mistral and DeepSeek with minimal GPU memory.
Run RLHF Direct Preference Optimization workflows to align model outputs with human feedback.
Perform supervised instruction tuning to build domain datasets and update weights for task-specific accuracy gains.
Compress large teachers into efficient students via knowledge distillation, transferring soft targets and hidden-feature signals.

Requirements
Python
Basic of Generative AI

Description
Master the complete spectrum of Large Language Model fine-tuning with the most comprehensive hands-on course available today. This intensive program transforms you from foundational concepts to enterprise-level deployment, covering cutting-edge techniques across multiple architectures and cloud platforms.What You’ll LearnAdvanced Fine-Tuning Methodologies:Master LoRA (Low-Rank Adaptation) for parameter-efficient training that reduces computational costs while maintaining model performance23Implement QLoRA (Quantized LoRA) for memory-optimized fine-tuning in resource-constrained environmentsDeploy RLHF (Reinforcement Learning ) to create aligned AI systems that follow human preferencesApply DPO (Direct Preference Optimization) for improved model behavior without complex reinforcement learning pipelinesApply Model Distillation for Knowledge transfer from a large model to a smaller modelMulti-Architecture Model Training:Fine-tune BERT models for specialized text understanding and classification tasksCustomize Mistral models for domain-specific applications requiring efficient performanceAdapt GPT architectures for conversational AI  text generation systemsOptimize LLaMA models for professional-grade applicationsConfigure Cohere models for production-ready natural language processing workflowsDeploy on Hugging Face Hub: Master model uploading, versioning, and sharing using push_to_hub() functionality for seamless model distributionEnterprise Cloud Platform Mastery:Azure AI Foundry: Build, deploy, and manage enterprise-grade AI applications with integrated development environmentsAWS Bedrock: Implement scalable fine-tuning workflows using S3, Lambda, and API Gateway for AI-powered applicationsGCP Vertex AI: Leverage parameter-efficient tuning and full fine-tuning approaches with supervised learning methodologiesKey Learning OutcomesTransform your AI expertise through hands-on projects that simulate real-world enterprise scenarios. Experience comprehensive dataset preparation, from raw data to production-ready training formats. Master performance optimization techniques including hyperparameter tuning, model evaluation metrics, and cost management strategies across cloud platforms. Build end-to-end deployment pipelines that scale from prototype to enterprise production environments.Course JourneyBegin with transformer architecture fundamentals before progressing through parameter-efficient training methodologies. Each technique is reinforced through practical coding sessions using industry-standard datasets and real-world use cases. Experience comprehensive cloud platform integration across Azure, AWS, and GCP ecosystems, learning platform-specific optimization strategies and cross-platform migration techniques.Who Should EnrollDesigned for intermediate to advanced AI practitioners, including machine learning engineers, data scientists, AI researchers, and software developers seeking specialization in LLM customization. Basic Python programming knowledge and familiarity with machine learning concepts are recommended.

Who this course is for
Fine tune LLMs

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