My Journey to a serverless transformers pipeline on Google Cloud

Back to Articles My Journey to a serverless transformers pipeline on Google Cloud Published March 18, 2021 Update on GitHub Upvote 2 Dominici Maxence Follow guest A guest blog post by community member Maxence Dominici This article will discuss my journey to deploy the transformers sentiment-analysis pipeline on Google Cloud. We will start with a quick introduction to transformers and then move to the technical part of the implementation. Finally, we'll summarize this implementation and review what we have achieved. The Goal I wanted to create a micro-service that automatically detects whether a customer review left in Discord is positive or negative. This would allow me to treat the comment accordingly and improve the customer experience. For instance, if the review was negative, I could create a feature which would contact the customer, apologize for the poor quality of service, and inform him/her that our support team will contact him/her as soon as possible to assist him and hopefully fix the problem. Since I don't plan to get more than 2,000 requests per month, I didn't impose any performance constraints regarding the time and the scalability. The Transformers library I was a bit confused at the beginning when I downloaded the .h5 file. I thought it would be compatible with tensorflow.keras.models.load_model, but this wasn't the case. After a few minutes of research I was able to figure out that the file was a weights checkpoint rather than a Keras model. After that, I...