Course Outline

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

Requirements

  • 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 Hours

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