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코스 개요
Introduction to Enterprise Localization with LLMs
- Understanding enterprise localization ecosystems
- From NMT to LLM-driven translation
- Challenges of quality, governance, and compliance
LLM Model Landscape for Localization
- Comparison of Deepseek, Qwen, Mistral, and OpenAI models
- Fine-tuning and adaptation for translation and post-editing
- Model deployment and cost-performance considerations
Architecting LLM Localization Pipelines
- System design patterns for LLM-based translation
- Connecting APIs, databases, and content management systems
- Pipeline orchestration using LangChain and Docker
Automated Quality Assurance for LLM Translations
- Defining linguistic quality metrics (BLEU, COMET, MQM)
- Building automated QA agents for translation validation
- Post-editing feedback loops and continuous improvement
Governance and Compliance in Localization AI
- Establishing human-in-the-loop governance
- Tracking, audit logs, and change control
- Ethical and data privacy standards in LLM systems
Evaluation and Monitoring Frameworks
- Monitoring translation performance and drift
- Real-time alerting and logging with open-source tools
- Implementing review dashboards for QA oversight
Enterprise Integration and Workflow Automation
- Integrating LLM translation pipelines with CMS and TMS systems
- Workflow automation and job scheduling
- Cross-departmental collaboration and version control
Scaling and Securing Localization Infrastructure
- Scaling multi-model deployments in cloud and on-premises
- Security, access management, and data encryption
- Governance best practices for enterprise-wide LLM adoption
Summary and Next Steps
요건
- An understanding of machine learning and natural language processing
- Experience with Python or TypeScript for API integration
- Familiarity with enterprise localization workflows and tools
Audience
- AI and NLP Engineers
- Localization Technology Managers
- Software Architects and Engineering Leads
21 시간
