Which Machine Translation API to choose for your project?
Tags: machinetranslation, api
Published: 2022-08-05 13:53:23

_This article is brought to you by the Eden AI team. We allow you to test and use in production a large number of AI engines from different providers directly through our API and platform. You are a solution provider and want to integrate Eden AI, contact us at: contact@edenai.co_

In this article, we are going to see how we can easily integrate a Machine Translation engine in your project and how to choose and access the right engine according to your data.


## Definition:

Machine Translation (MT) or automated translation is a process when a computer software translates text from one language to another without human involvement. There are three types of machine translation methods: rule-based, statistical and neural networks. urrent machine translation software often allows for customization by domain or profession, improving output by limiting the scope of allowable substitutions.

## History:

In 1951, Yehoshua Bar-Hillel was the first researcher in Machine Translation. He began his research at MIT. A Georgetown University MT research team followed with a public demonstration of its Georgetown-IBM experiment system in 1954.

During the 60s, researchers continued to study MT but real progress was really slow.

Beginning in the late 1980s, as computational power increased and became less expensive, more interest was shown in statistical models for machine translation. MT became more popular after the advent of computers. Various computer based translation companies were launched, including Trados.

By 1996, MT on the web started with SYSTRAN offering free translation of small texts (1996) The second free translation service on the web was Lernout & Hauspie’s GlobaLink. More innovations during this time included MOSES, the open-source statistical MT engine (2007), a text/SMS translation service for mobiles in Japan (2008), and a mobile phone with built-in speech-to-speech translation functionality for English, Japanese and Chinese (2009).

In 2012, Google announced that Google Translate translates roughly enough text to fill 1 million books in one day.

## Use cases:

You can use Machine Translation in numerous fields, here are some examples of common use cases:

- documentation and instructions: translate PowerPoint presentations, intranet bulletins, and other similar documents
- email and communication: help decrease or even eliminate the language barrier in communication. A translation of the text can be obtained quickly and in a form that enables the recipients to understand the core content of the message

### The Multi cloud approach

When you need a Machine Translation engine, you have 2 options:

- First option: multiple open source Machine Translation engines exist, they are free to use. Some of them can be performant but it can be complex to set up and use. Using an open source AI library requires data science expertise. Moreover, you will need to set up a server internally to run open source engines.
- Second option: you can use engines from your cloud provider. Actually, cloud providers like Google Cloud, AWS, Microsoft Azure, Alibaba Cloud or IBM Watson are all providing multiple AI engines including Machine Translation. This option looks very easy because you can stay in a known environment where you might have abilities in your company and the engine is ready-to-use.

The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.

This method is the best in terms of performance and optimization but it presents many inconveniences:

- you may not know every performant providers on the market
- you need to subscribe and contract with all providers
- you need to master each providers API documentation
- you need to check their pricings
- You need to process data in each engine to realize the benchmark

Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Machine Translation. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.

Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Machine Translation engines with a provider as a simple parameter. With only a few lines, you can set up your project in production:
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{% embed https://www.youtube.com/watch?v=yPuf_sX3Avk %}

### Test and API:

Here is the code in Python (GitHub repo) that allows to test Eden AI for face detection:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/7f4o589j1sabbajtt7e7.png) 
Eden AI SDK for Machine Translation

### Answer:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/991hk4vc8em7ueago2n5.png) 
Eden AI SDK for Machine Translation
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### Platform:

Eden AI also allows you to compare these engines directly on the web interface without having to code:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/y3aj2zzjgybdvaf67ebv.png) 
Eden AI Platform for Machine Translation
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![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/hmvgod9jfc73emzzc944.png) 
Eden AI Platform for Machine Translation

## Conclusion:

There are numerous Machine Translation engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate Machine Translation technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.