LLM Fine-Tuning & AI Model Deployment
The LLM Fine-Tuning & AI Model Deployment course provides comprehensive training in customizing, optimizing, and deploying Large Language Models (LLMs) for real-world AI applications. This course...
The LLM Fine-Tuning & AI Model Deployment course provides comprehensive training in customizing, optimizing, and deploying Large Language Models (LLMs) for real-world AI applications. This course equips students with practical skills in transformer architectures, prompt tuning, parameter-efficient fine-tuning (PEFT), LoRA techniques, dataset preparation, model evaluation, inference optimization, and cloud deployment of AI models. Learners will gain hands-on experience using modern AI frameworks such as Hugging Face Transformers, PyTorch, LangChain, and deployment tools for scalable AI systems. Through practical projects and industry-oriented workflows, students will develop expertise in adapting pretrained AI models for business automation, conversational AI, NLP applications, and intelligent systems deployment.
- Understand the fundamentals of Large Language Models (LLMs) and transformer architectures
- Learn how pretrained AI models are trained, optimized, and adapted for specific tasks
- Perform dataset collection, preprocessing, annotation, and tokenization for LLM fine-tuning
- Fine-tune transformer-based models using Hugging Face Transformers and PyTorch
- Implement Parameter-Efficient Fine-Tuning (PEFT) and LoRA techniques
- Apply prompt tuning, instruction tuning, and supervised fine-tuning methods
- Evaluate AI model performance using NLP metrics and benchmarking techniques
- Optimize inference performance using quantization and model compression techniques
- Work with GPU environments and cloud-based AI development platforms
- Build conversational AI systems and domain-specific NLP applications
- Integrate LLMs into APIs, chatbots, and AI-powered business applications
- Use LangChain and vector databases for retrieval-augmented generation (RAG) workflows
- Deploy AI models using Docker, FastAPI, cloud services, and inference servers
- Implement AI model monitoring, scaling, and deployment best practices
- Understand AI ethics, bias mitigation, and responsible AI deployment practices
- Use Git and collaborative workflows in AI engineering projects
- Develop portfolio-ready LLM applications and deployment case studies
- Gain practical exposure to modern MLOps and AI infrastructure workflows
- Explore real-world applications of generative AI across industries
- Prepare for careers in AI engineering, NLP engineering, MLOps, and generative AI development
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