This article will be used to wrap up the series, by building a neural machine translation web app and then actually deploying it.

Well, the steps used here to deploy the model is applicable to all Language models.

Let’s Get started:

After spending a lot of time gathering and cleaning data and developing and tuning your model, it’s now time to deploy the model and make it ready for your users to interact with.

Here are the series of questions you must ask yourself as this stage of the process, which actually need an answer:

• How do I present the model on the web?
• How does the web interface interact with the model?
• What cloud service do I use?
• How do I debug errors after deployment?
• How do I scale the app?

This article will be answering the above questions—except for the last. I will leave the scaling of the app for another day, as that topic warrants its own investigation.

Goals/Steps

• Saving your model and word vocab
• Building a web interface for the app
• Dockerizing the model
• Deploying the model to Google Cloud
• Debugging errors in the deployed app

#pytorch #nlp #deep-learning #heartbeat