Hello World 👋

I’m Vasil, a DevOps Engineer with a passion for building reliable, scalable, and well-architected cloud platforms. With hands-on experience across cloud infrastructure, CI/CD, observability, and platform engineering, I enjoy turning complex operational challenges into clean, automated solutions.

I’ve been working with AWS Cloud for over 5 years, and I believe it’s high time I start exploring AI on AWS more deeply. Through these posts, I plan to share practical learnings, real-world experiences, and honest perspectives from my journey in DevOps, Cloud, and now AI.

Without further delay — let’s dive in 🚀

Introduction

As someone who writes about AWS in English, I wanted to explore whether Amazon Translate could help make technical AWS content accessible to regional-language audiences.

Instead of assuming it would “just work,” I approached this as an experiment:

• Can Amazon Translate handle technical paragraphs?
• How does it perform for regional Indian languages like Marathi?
• How does that compare with a more widely supported language like Hindi? This post documents what actually happens when you try this in practice — including the limitations.

A real world architecture would look something like this

This diagram shows a simple, real-world flow for publishing AWS content in regional languages.

An author writes the original article in English and stores it as a text or markdown file in Amazon S3. That content is then passed to Amazon Translate, which converts it into Marathi. The translated output is stored back in S3 and can be published to platforms like Medium, dev.to, or internal documentation portals.

You may notice AWS Lambda in the diagram as an optional component. In real production setups, Lambda is often used to automate this workflow (for example, triggering translation when a new file is uploaded).

However, in this article we intentionally keep things simple and interact with Amazon Translate directly using the AWS CLI, without introducing Lambda or additional automation. This keeps the focus on understanding the service itself before adding more moving parts.

Pre-requisites

You’ll need:

• An AWS account
• AWS CLI configured locally (I’ll be using Cloudshell)
• Basic familiarity with AWS services

Let’s begin!

Test Input (English Paragraph)

To keep things realistic, I used a full paragraph from AWS documentation-style content:

What is Amazon Translate? Amazon Translate lets you localize content for diverse global users and translate and analyze large volumes of text to activate cross-lingual communication between users. Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation.

Marathi Translation (Observed Behavior)

Command used:

aws translate translate-text \
--region us-east-1 \
--source-language-code en \
--target-language-code mr \
--text "What is Amazon Translate? Amazon Translate lets you localize content for diverse global users and translate and analyze large volumes of text to activate cross-lingual communication between users. Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation."

Actual output:

Amazon भाषांतर म्हणजे काय? Amazon Translate आपल्याला विविध जागतिक वापरकर्त्यांसाठी सामग्री स्थानिकीकरण करण्यास आणि वापरकर्त्यांमधील क्रॉस-भाषिक संप्रेषण सक्रिय करण्यासाठी मोठ्या Amazon Translate ही एक न्यूरल मशीन भाषांतर सेवा आहे जी जलद, उच्च-गुणवत्तेची, परवडणारी

What’s going on here?

• The paragraph is clearly truncated
• Sentences merge abruptly
• The translation cuts off before completing the final thought
• Technical flow and readability suffer The generated output in no way can be used directly and cannot be even given to an LLM for further fine tuning because the input itself isn’t complete and lacks flow. (So all in all this requires heavy human intervention)

Hindi Translation (Same Paragraph)

Using the exact same input:

aws translate translate-text \
--region us-east-1 \
--source-language-code en \
--target-language-code hi \
--text "What is Amazon Translate? Amazon Translate lets you localize content for diverse global users and translate and analyze large volumes of text to activate cross-lingual communication between users. Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation."

Actual output:

Amazon Translate क्या है? Amazon Translate से आप विभिन्न वैश्विक उपयोगकर्ताओं के लिए सामग्री का स्थानीयकरण कर सकते हैं और उपयोगकर्ताओं के बीच अंतर-भाषी संचार को सक्रिय करने के लिए बड़ी मात्रा में टेक्स्ट का अनुवाद और विश्लेषण कर सकते हैं। Amazon Translate एक न्यूरल मशीन अनुवाद सेवा है जो तेज़, उच्च-गुणवत्ता, किफायती और अनुकूलन योग्य भाषा अनुवाद प्रदान करती है।

Observations

• Complete paragraph
• Proper sentence boundaries
• Natural flow
• Technically accurate This is publishable with minimal human editing.

Translation for Indian Regional Languages: A Broader Challenge

It’s important to call out that what we’re seeing here is not unique to Amazon Translate.

High-quality translation for Indian regional languages has always been a hard problem, even outside AWS and cloud services. This challenge shows up across traditional NLP systems and modern generative AI models alike.

Some of the reasons include:

• Linguistic complexity Languages like Marathi have rich morphology, flexible sentence structures, and context-heavy grammar. Direct sentence-to-sentence mapping from English often loses meaning or flow.
• Limited high-quality training data Compared to English or Hindi, regional languages have significantly fewer large, clean, technical corpora available for training translation models.
• Technical vocabulary mismatch Cloud and software terminology often has no commonly accepted regional equivalent. Models must decide whether to transliterate, translate, or drop context entirely — which can lead to broken sentences.
• Mixed-language expectations In real-world usage, Indian technical writing often mixes English service names with regional language explanations. Handling this hybrid style consistently is still difficult for automated systems.

What This Means in the Real World

PLEASE NOTE!

Like I said, what we’re seeing here isn’t a failure of Amazon Translate — it’s a reflection of the broader state of machine translation for Indian regional languages today.

• Amazon Translate does support Marathi along with several other Indian regional languages, but for long technical paragraphs, the output can be unreliable.
• Hindi performs significantly better for the same technical content. Breaking content into multiple smaller calls is possible, but:
• It’s inefficient
• Not scalable
• Still doesn’t guarantee quality This is important to know before committing to a regional-language publishing workflow.

$Practical Takeaway If you’re planning to localize content or technical documentation using AWS Translate then:

Hindi: Viable today for paragraph-level technical content Marathi (and similar regional languages): Needs improvement before it can be used confidently without heavy human intervention A realistic approach today would be:

• Use Amazon Translate for exploration and drafts
• Rely on human review and editing for regional languages
• Avoid assuming parity across all supported languages

Final Thoughts

This experiment wasn’t about proving that Amazon Translate is perfect — it’s about understanding where it works well and where it still struggles.

For me, the takeaway is clear:

• Amazon Translate is strong for widely used languages
• Regional technical localization is still a work in progress

And that’s okay — knowing the limits is just as valuable as knowing the features.