In today’s AI-driven world, optimizing large language models for specific domains while managing cost is a key competitive skill. This course trains AI engineers, ML practitioners, and data scientists to transform baseline generative models into efficient, production-ready solutions. Through hands-on labs using Hugging Face Transformers, PEFT, and Evaluate, you’ll master decoding strategies (temperature, top-k, top-p, beam search), automated evaluation (BLEU, ROUGE, BERTScore, custom metrics), and parameter-efficient fine-tuning (LoRA) that cuts trainable parameters by 99% without losing quality. Real-world projects cover fine-tuning 7B+ models for legal, medical, and financial applications while analyzing GPU and inference costs. The capstone simulates real constraints—limited GPU memory, latency, budget, and compliance—requiring technical, analytical, and executive deliverables. By course end, you’ll confidently optimize and evaluate LLMs, balancing quality, performance, and cost for advanced roles in LLM engineering, MLOps, and AI product development.
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Fine-Tune & Optimize Generative AI Models
包含在 中
您将学到什么
Apply decoding strategies (e.g., temperature, top-k, top-p, beam search) to control model outputs for quality, diversity, and relevance.
Evaluate AI-generated text using automated metrics and frameworks to systematically assess fluency, coherence, and factual accuracy.
Implement parameter-efficient fine-tuning (PEFT) techniques to create domain-adapted foundation models while balancing cost-performance trade-offs.
您将获得的技能
- Artificial Intelligence and Machine Learning (AI/ML)
- Analysis
- Model Based Systems Engineering
- Transfer Learning
- AI Personalization
- Model Evaluation
- Model Deployment
- Program Evaluation
- AI Product Strategy
- Large Language Modeling
- Performance Tuning
- MLOps (Machine Learning Operations)
- Generative AI
- Hugging Face
- Applied Machine Learning
- Responsible AI
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有3个模块
This module introduces learners to decoding strategies and parameters that control how generative AI models produce text. Learners will explore the mechanics of temperature, top-k, top-p sampling, and beam search, understanding how these parameters influence output diversity, coherence, and relevance. Through hands-on experimentation, learners will gain practical skills in tuning these parameters for different use cases.
涵盖的内容
5个视频2篇阅读材料1次同伴评审
This module equips learners with systematic approaches to evaluate AI-generated text using automated metrics and evaluation frameworks. Learners will explore metrics like BLEU, ROUGE, perplexity, BERTScore, and task-specific evaluation methods, understanding both their capabilities and limitations. The module emphasizes when automated metrics suffice and when human evaluation remains essential.
涵盖的内容
4个视频1篇阅读材料1次同伴评审
This module introduces learners to parameter-efficient fine-tuning (PEFT) techniques that enable domain adaptation of large language models without the computational and memory costs of full fine-tuning. Learners will explore methods like LoRA, prefix tuning, and adapter layers, understanding the cost-performance trade-offs and practical implementation strategies for real-world applications.
涵盖的内容
4个视频1篇阅读材料1个作业2次同伴评审
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