Transform your AI expertise from experimental to enterprise-ready with this comprehensive course on building and deploying production-grade LLM applications. Master the complete lifecycle from architecture selection to scalable deployment, learning to choose optimal models (GPT, BERT, T5) based on real business constraints like latency, cost, and domain requirements. Gain hands-on expertise with parameter-efficient fine-tuning techniques, especially LoRA, that deliver enterprise performance improvements while reducing computational costs by up to 90%. Using industry-standard tools like Hugging Face Transformers, you'll implement complete fine-tuning pipelines, design secure production architectures, and build robust monitoring systems that ensure 99.9% uptime. Through scenario-based labs, you'll solve real-world challenges in customer service automation, financial document analysis, and healthcare AI.
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Build & Adapt LLM Models with Confidence
Dieser Kurs ist Teil von Spezialisierung für Build Next-Gen LLM Apps with LangChain & LangGraph
Dozenten: Starweaver
Bei enthalten
Empfohlene Erfahrung
Was Sie lernen werden
Analyze LLM architectures and foundation models for specific use cases.
Implement fine-tuning techniques using industry-standard tools and frameworks.
Deploy LLM models in production environments with security and optimization.
Kompetenzen, die Sie erwerben
- Kategorie: Model Deployment
- Kategorie: Transfer Learning
- Kategorie: Application Security
- Kategorie: Prompt Engineering
- Kategorie: Model Evaluation
- Kategorie: Scalability
- Kategorie: Cloud Deployment
- Kategorie: Large Language Modeling
- Kategorie: MLOps (Machine Learning Operations)
- Kategorie: API Design
- Kategorie: LLM Application
- Kategorie: System Monitoring
- Kategorie: AI Security
- Kategorie: Applied Machine Learning
- Kategorie: Artificial Intelligence
- Kategorie: Hugging Face
- Kategorie: Performance Tuning
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 the foundational concepts of large language model architectures and their practical applications. Learners will explore the core transformer architecture, examining the trade-offs between encoder-only, decoder-only, and encoder-decoder models. They will develop expertise in evaluating model families like GPT, BERT, and T5 against specific business requirements, considering factors such as domain relevance, latency constraints, context length needs, and computational costs. By the end of this module, learners will confidently select and justify the most appropriate LLM architecture for real-world enterprise scenarios.
Das ist alles enthalten
4 Videos2 Lektüren1 peer review
This module focuses on mastering parameter-efficient fine-tuning techniques to adapt pre-trained LLMs for specialized domains and tasks. Learners will explore advanced methods like LoRA (Low-Rank Adaptation) and other parameter-efficient approaches that dramatically reduce computational requirements while maintaining model performance. Through hands-on experience with industry-standard frameworks like Hugging Face Transformers, learners will master the complete fine-tuning workflow: from data preparation and preprocessing to training configuration, evaluation metrics, and deployment optimization. The module emphasizes practical skills for building domain-adapted models that achieve enterprise-grade performance while balancing accuracy, efficiency, and cost-effectiveness.
Das ist alles enthalten
3 Videos1 Lektüre1 peer review
This module explores the full deployment pipeline for LLM applications with a focus on scalability, performance, and security. Learners will design serving architectures using APIs and streaming endpoints, integrate enterprise data, and apply retrieval with FAISS. Optimization practices such as caching, load balancing, and autoscaling are introduced to ensure efficiency at scale. Security is emphasized through OWASP guidelines, strong authentication, and defenses against prompt injection attacks. Finally, learners implement monitoring and alerting systems to maintain reliability, compliance, and trust in production environments.
Das ist alles enthalten
4 Videos1 Lektüre1 Aufgabe2 peer reviews
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Status: Kostenloser Testzeitraum
Status: Kostenloser Testzeitraum
Status: Kostenloser Testzeitraum
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