# machine-translation Published articles for machine-translation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From Zero to Hero: Localization-Led Generative AI at Udemy DevFeed: [From Zero to Hero: Localization-Led Generative AI at Udemy]() Original publisher: [Read original article]() Author: Nishanth Gandhidoss Published: 2025-09-22T17:47:18Z Content type: article Language: en Sources: [Udemy Engineering]() Topics: [Localization (l10n)](), [Generative AI](), [Large Language Model](), [datasets]() Tags: [ai](), [features](), [generative-ai](), [generative-ai-tools](), [implementation](), [llm](), [llm-applications](), [machine-learning](), [machine-translation](), [retrieval-augmented-gen](), [techniques](), [translation]() ### AI overview Udemy describes how it localized generative AI features, including Skills Mapping and its in-course AI Assistant, to serve learners in multiple languages. The article compares translation management systems, multilingual LLMs, and hybrid strategies, emphasizing trade-offs among implementation speed, language coverage, fluency, cultural nuance, and domain precision. ### Source excerpt By Nishanth Gandhidoss, Senior Staff Data Scientist @ Udemy Why Multilingual Generative AI Matters Generative AI is rapidly changing how people learn and work, but those gains are only realized when the technology speaks the user's language. Over the past year at Udemy, we prioritized breaking the English-only barrier so our Gen AI features, most critically Skills Mapping and the in-course AI Assistant, could deliver value for learners across the globe. Our approach balanced speed, coverage, and quality, allowing us to move quickly without sacrificing fidelity in markets where nuance matters. Below we describe the localization strategy we adopted, why we made those trade-offs, and the operational practices that made the rollout repeatable, scalable, and quick. Localization Strategies When deciding how to localize a Gen AI product, it is helpful to think of options on a spectrum from low to high complexity. The simplest approach provides broad language coverage fast, while the most involved approach optimizes for fluency, cultural nuance, and domain precision. At Udemy, we researched and organized our work around three primary strategies: a translation management system (TMS) for rapid implementation with broader coverage, a multilingual LLM system (MLS) for high-fidelity experiences, and a hybrid approach to balance reach and quality. As each strategy is introduced below, we briefly explain the industry-standard localization techniques they draw on, with examples, so the differences are clear. Translation Management System (TMS) At the simplest end of the spectrum, we have the TMS approach. With a TMS, the user interacts in their own language, but the AI model itself operates in English input/output. The system first detects the user's input language, say, in Spanish, and sends the input through a machine translation (MT) service that converts it into English. The LLM then generates an English response, and that response is translated back into the user's input lang ## Build a self-hosted, GPU-accelerated translation API with open-source LLMs DevFeed: [Build a self-hosted, GPU-accelerated translation API with open-source LLMs]() Original publisher: [Read original article]() Author: fm.mateo Published: 2025-08-07T12:57:31Z Content type: tutorial Language: en Sources: [Homegate Engineering Blog - Medium]() Topics: [Low Latency](), [Large Language Model](), [Cloud](), [Google Cloud Platform (GCP)](), [GPU](), [Inference](), [Open Source](), [Self-hosted](), [API](), [Back end](), [Monitoring](), [Tooling]() Tags: [ai](), [amazon](), [api](), [backend](), [cloud](), [cost](), [deepl](), [energy](), [google-translate](), [inference](), [infrastructure](), [latency](), [low-latency](), [machine-translation](), [monitoring](), [open-source](), [tooling](), [translation]() ### AI overview This step-by-step guide describes how Swiss Marketplace Group built and deployed a self-hosted, GPU-accelerated translation service using open-source large language models on Google Cloud Platform. The article reports over 85% cost savings, sub-three-second latency, and no impact on buyers' conversion rate compared with its previous DeepL API usage. ### Source excerpt Build a low-cost, low-latency, low-energy AI Translator: a DeepL alternative that worksA step-by-step guide to deploying your own AI-powered translation API at scale, and get rid of DeepL, Google Translate, Amazon Translate, etc.Introduction and motivation At Swiss Marketplace Group (SMG), we operate multiple online platforms used daily by millions of people across Switzerland. One of them, Ricardo, is a second-hand marketplace where buyers and sellers interact in four different languages: French, German, Italian, and English. To deliver a seamless experience across languages, we had been relying on DeepL API, one of the most accurate translation APIs on the market. And while DeepL served us well in terms of quality and response time, it came with significant downsides: A monthly cost of over 7,000 CHF A black-box approach, offering no insight into how translations were produced Data privacy risks As usage grew, so did the bill, and the urgency to reduce it. Around that time, generative AI had made enormous improvements, and open-source large language models (LLMs) had become more accessible than ever. This sparked a question: Can we replace DeepL API with a self-hosted open-source LLM, without sacrificing translation quality, speed, and user experience? The answer turned out to be yes. Over the course of my master thesis, I built a production-grade, GPU-accelerated translation service using modern LLM tooling, hosted entirely on Google Cloud Platform (GCP), and fully integrated into Ricardo's infrastructure. The result: Over 85% cost savings Sub-3-second latency No impact on conversion rate of buyers And everything is reproducible, transparent, and extensible. In this article, I'll walk you through exactly how I built it, from selecting the right model, optimizing inference time, and wrapping it in an API, to deploying and monitoring it in production. My goal is simple: enable you to do the same. Whether you're a backend developer, a ML engineer, or just AI-curious