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코스 개요
Introduction to LLM Translation Systems
- Understanding neural machine translation (NMT) and its limitations
- Overview of LLM architectures and their translation capabilities
- Comparison between traditional MT and LLM-based translation
Working with Proprietary and Open-Source LLMs
- Using OpenAI, Deepseek, Qwen, and Mistral models for translation
- Performance and latency trade-offs
- Selecting the right model for your workflow
Building Translation Pipelines with LangChain
- Pipeline design principles for LLM translation
- Implementing a translation chain with LangChain
- Managing context windows and token usage
Automating Translation Workflows
- Scheduling translation tasks using Python and automation tools
- Handling multi-language batch jobs
- Integration with localization management systems
Enhancing Translation Quality
- Prompt engineering for context-aware translation
- Post-editing automation and human-in-the-loop design
- Fine-tuning strategies for domain-specific translation
Evaluating and Monitoring Translation Pipelines
- Automatic quality estimation (AQE) and BLEU score evaluation
- Logging, analytics, and pipeline observability
- Error handling and fallback mechanisms
Scaling and Deploying Translation Systems
- Cloud deployment with Docker and serverless frameworks
- Load balancing and parallel processing for large-scale translation
- Security, compliance, and data privacy considerations
Integrating Translation Pipelines into Enterprise Infrastructure
- Connecting translation APIs to CMS, ERP, and L10n platforms
- Managing costs and performance at scale
- Governance and approval workflows for enterprise localization
Summary and Next Steps
요건
- An understanding of Python programming
- Experience with API integration and workflow automation
- Familiarity with machine learning concepts and language models
Audience
- Machine Learning Engineers
- Localization and Translation Technology Specialists
- Software Architects and Engineering Leads
21 시간
