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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您将学到什么
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.
您将获得的技能
- Model Deployment
- Transfer Learning
- Application Security
- Prompt Engineering
- Model Evaluation
- Scalability
- Cloud Deployment
- Large Language Modeling
- MLOps (Machine Learning Operations)
- API Design
- LLM Application
- System Monitoring
- AI Security
- Applied Machine Learning
- Artificial Intelligence
- Hugging Face
- Performance Tuning
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 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.
涵盖的内容
4个视频2篇阅读材料1次同伴评审
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.
涵盖的内容
3个视频1篇阅读材料1次同伴评审
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.
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
获得职业证书
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