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.
Course 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 Learn
Advanced Fine-Tuning Methodologies:
- Master LoRA (Low-Rank Adaptation) for parameter-efficient training that reduces computational costs while maintaining model performance23
- Implement QLoRA (Quantized LoRA) for memory-optimized fine-tuning in resource-constrained environments
- Deploy RLHF (Reinforcement Learning ) to create aligned AI systems that follow human preferences
- Apply DPO (Direct Preference Optimization) for improved model behavior without complex reinforcement learning pipelines
- Apply Model Distillation for Knowledge transfer from a large model to a smaller model
Multi-Architecture Model Training:
- Fine-tune BERT models for specialized text understanding and classification tasks
- Customize Mistral models for domain-specific applications requiring efficient performance
- Adapt GPT architectures for conversational AI text generation systems
- Optimize LLaMA models for professional-grade applications
- Configure Cohere models for production-ready natural language processing workflows
- Deploy on Hugging Face Hub: Master model uploading, versioning, and sharing using push_to_hub() functionality for seamless model distribution
Enterprise Cloud Platform Mastery:
- Azure AI Foundry: Build, deploy, and manage enterprise-grade AI applications with integrated development environments
- AWS Bedrock: Implement scalable fine-tuning workflows using S3, Lambda, and API Gateway for AI-powered applications
- GCP Vertex AI: Leverage parameter-efficient tuning and full fine-tuning approaches with supervised learning methodologies
Key Learning Outcomes
Transform 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 Journey
Begin 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 Enroll
Designed 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.
