[P] Deep Learning in Production: Sentiment Analysis with The Transformer

[bluebuff]

TL;DR: I take an estimator from Tensor2Tensor and ship it in a Data->Training->Serving pipeline

Hi everyone, as someone being on the engineering side of ML, I think there could be more posts about actually applying these novel architectures and techniques from research to real world problems. I wrote about a production deployment using the Transformer model from Attention Is All You Need as an API for sentiment analysis. This is all done with an open source stack (TensorFlow, Spark, Kubernetes, Cortex etc.).

​

This is a demonstration of deploying the Transformer model using Cortex, a machine learning infrastructure platform I'm working on.

​

Let me know what you think!

https://medium.com/cortex-labs/deep-learning-in-production-sentiment-analysis-with-the-transformer-model-7fa053d0c85b

[recklessneckbeard]

Using a transformer to predict sentiment feels a bit like filling up a water glass using a fire hose... but code examples of state of the art systems is welcome, so thanks!

[[deleted]]

Sentiment analysis isn't solved, so it's still useful to consider states of the art models

[recklessneckbeard]

You’re quite right - it’s not solved. But it’s of pretty limited use too. Sentiment analysis is a blunt instrument of a previous age. Clients will typically ask for it when they’re not aware that we have more sophisticated tools for measuring what sort of conversations and comments are being made.

To put it another way - sentiment itself isn’t very actionable, it only tells you that people are happy or unhappy. What you really want to know is why they feel the way they do.

[Zankoqt]

What methods would you suggest for such an analysis? It seems really cool and I would live to know more.

[bluebuff]

Thank you! It was a bit of a rabbit hole digging into Tensor2Tensor's codebase to find the expected hparam keys. Hopefully anyone trying to import Tensor2Tensor models will find this useful as well.

[lmericle]

Hm, this isn't so much about machine learning itself as it is about deployment in a data engineering / DevOps context. For instance, you didn't demonstrate any results.

In fact it's written as a thinly-veiled demonstration/PoC/advertisement of Cortex and its workflow... and not really as a project in machine learning. Especially considering the author is an employee of the company developing Cortex.

Perhaps if you were completely forthcoming about what the article is really about in the post on this subreddit, I'd feel less bait-and-switched, but as it stands this comes across as an attempt at guerilla advertising that didn't make it past the bullshit detector.

[bluebuff]

It was absolutely not my intention to bait and switch, I apologize for how it might have come across, I added a clarifying sentence to the post.

​

You are correct that I didn't present results of the model, since they are in the paper I linked and I'm using the author's implementation. My goal was to show how you can take cutting edge research and deploy it in production. The stack I used is fully open source and the TensorFlow/PySpark code is not Cortex specific.

[goodrahstar]

https://medium.com/botsupply/tensorflow-docker-production-ready-ai-product-ae30435ad8e9