Amazon SageMaker vs. Amazon Translate

It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.

Incentivized

I use Amazon Translate plug-in in my regular translation
work. I realized that it does better in marketing and general texts. It still
suffers from some shortcomings when it comes to the specific linguistic
feature of Arabic. It makes some grammatical mistakes. It does not sound
natural in some instances because of using uncommon word combinations.

Incentivized

• Machine Learning at scale by deploying huge amount of training data
• Accelerated data processing for faster outputs and learnings
• Kubernetes integration for containerized deployments
• Creating API endpoints for use by technical users

Incentivized

• Medical texts
• Neural machine translation
• General texts

Incentivized

• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

Incentivized

• Arabic syntax
• Collocations

Incentivized

Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

Incentivized

I use Google Translate and Amazon Translate. I find them both helpful. What I like more about Amazon Translate is that I can control the privacy of my files. The text entered into Google Translate enters its database and might be used for further improvement of their service; however, I do not like exposing the data of my clients to a third party. Therefore, I find Amazon Translate wins by large in this regard.

Incentivized

• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

Incentivized

• Affordable fees
• A free tier

Incentivized