Best Practices Intermediate

Building reliable, cost-effective translation systems for production requires careful attention to architecture, edge cases, and quality assurance. This lesson covers the key practices for deploying machine translation at scale.

Architecture for Production

Production Architecture Checklist:
  • Caching — Cache translations to avoid re-translating identical text (use Redis or a database)
  • Rate limiting — Respect API rate limits with exponential backoff and request queuing
  • Fallback chain — If the primary API fails, fall back to a secondary service or local model
  • Language detection — Auto-detect source language before translating; do not assume
  • Async processing — Use background jobs for batch translation to avoid blocking user requests

Handling Edge Cases

Edge Case Problem Solution
HTML/Markup Tags get corrupted or translated Use APIs' HTML mode or extract text, translate, reinsert
Named entities Names, brands get incorrectly translated Use glossaries or wrap entities in non-translatable tags
Placeholders Variables like {name} get mangled Replace with unique tokens before translation, restore after
Mixed languages Text contains multiple languages Split by language using detection, translate segments separately
Short text Buttons, labels lack context for good translation Provide context hints or use human translation for UI strings

Cost Optimization

Python 
import hashlib
import redis

r = redis.Redis()

def translate_with_cache(text, source_lang, target_lang, translate_fn):
    # Create a cache key from the text and language pair
    cache_key = hashlib.md5(
        f"{source_lang}:{target_lang}:{text}".encode()
    ).hexdigest()

    # Check cache first
    cached = r.get(cache_key)
    if cached:
        return cached.decode()

    # Translate and cache for 30 days
    translation = translate_fn(text, source_lang, target_lang)
    r.setex(cache_key, 86400 * 30, translation)
    return translation

Quality Assurance Pipeline

  1. Automated checks

    Run BLEU/COMET on a test set with every model update. Flag regressions automatically.

  2. Human spot-checks

    Sample 1-5% of translations weekly for human review. Focus on high-traffic content.

  3. User feedback

    Add a "report bad translation" button. Track feedback by language pair to identify problem areas.

  4. A/B testing

    When switching models or APIs, run both in parallel and compare quality before fully migrating.

When to Use Human Translation

Use human translation for:
  • Legal documents, contracts, and regulatory content
  • Marketing copy and brand messaging
  • Medical information where errors could harm patients
  • Creative content (literature, poetry, advertising slogans)
Use machine translation for:
  • User-generated content (reviews, comments, forum posts)
  • Internal communications and documentation
  • Real-time chat and customer support
  • Large-scale content that would be cost-prohibitive to translate manually

Course Complete!

You now have the knowledge to build, evaluate, and deploy machine translation systems. Return to the course overview to review any lessons or explore other AI School courses.

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