Data is the [lifeblood of any successful machine learning model](https://news.yahoo.com/data-matters-machine-learning-acquire-150000391.html?guccounter=1), and machine translation models are unsurprisingly no exception. Without [relevant and properly labelled data](https://www.definedcrowd.com/speed-your-time-to-market-with-off-the-shelf-data-you-can-trust/), even the most sophisticated machine translation model will be unable to achieve reliable high-quality results.

That being said, getting [hold of the right data](https://www.definedcrowd.com/overcoming-the-challenges-of-crowdsourcing-ai-training-data/) can be the most challenging part of a project, especially if you’re trying to do something entirely new—such as [building machine translation for rare, under-resourced languages](https://venturebeat.com/2020/10/19/facebooks-open-source-m2m-100-model-can-translate-between-100-different-languages/). Open source data, while great for academic projects and bootstrapping minimum viable product/proof-of-concept models, are often plagued with shoddy quality data samples. Worst still is the lack of quality controls, baking in biases that may go undetected until deployment. Don’t let your well-intentioned model land you in hot water—learn why quality is key to robust models and business success.

In this white paper, we will explore how to [address these challenges](https://www.defined.ai/blog/machine-translation-101/) by showing you how to create a perfect dataset for machine translation models, how to do data cleaning for machine translation training data, and how to perform machine translation evaluation once your model is trained and ready to be deployed.

Don’t wait—learn all this insightful information and more by downloading the white paper below!

**[Download the White Paper](https://prdstrapimediastorage.blob.core.windows.net/prdstrapimediastorage/assets/Machine_Translation_101_31bd2d7135.pdf)**

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