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
Dieser Kurs ist Teil von Spezialisierung für Build Next-Gen LLM Apps with LangChain & LangGraph
Dozenten: Sonali Sen Baidya
Bei enthalten
Empfohlene Erfahrung
Was Sie lernen werden
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.
Kompetenzen, die Sie erwerben
- Kategorie: Artificial Intelligence and Machine Learning (AI/ML)
- Kategorie: Analysis
- Kategorie: Model Based Systems Engineering
- Kategorie: Transfer Learning
- Kategorie: AI Personalization
- Kategorie: Model Evaluation
- Kategorie: Model Deployment
- Kategorie: Program Evaluation
- Kategorie: AI Product Strategy
- Kategorie: Large Language Modeling
- Kategorie: Performance Tuning
- Kategorie: MLOps (Machine Learning Operations)
- Kategorie: Generative AI
- Kategorie: Hugging Face
- Kategorie: Applied Machine Learning
- Kategorie: Responsible AI
Wichtige Details
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Dezember 2025
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- Erwerben Sie ein Berufszertifikat zur Vorlage
In diesem Kurs gibt es 3 Module
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.
Das ist alles enthalten
5 Videos2 Lektüren1 peer review
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.
Das ist alles enthalten
4 Videos1 Lektüre1 peer review
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.
Das ist alles enthalten
4 Videos1 Lektüre1 Aufgabe2 peer reviews
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