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Learn to Deploy a Machine Learning Model for the Abstractive Text Summarization on Google Cloud Platform (GCP)
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Business Objective
Text summarization is the process of automatically generating natural language summaries from an input document while retaining the important points. Abstractive Summarization is a task in NLP that aims to generate a concise summary of a source text. Unlike the extractive summarization technique, abstractive summarization does not simply copy essential phrases from the source text but also potentially come up with new relevant phrases, which can be seen as paraphrasing.
This ML model deployment project makes two initial assumptions, first that there is an ML model built (Transformer model in our case), and second there is an interface available for the model (Flask app)
We aim to create the ML model deployment project by using the google cloud platform (GCP). Here, GCP is used as a cloud provider. But first, we would suggest you go through the former project Abstractive Text Summarization using Transformers-BART Model before starting this project.
Aim
To deploy the machine learning model for the ‘Abstractive Text Summarization using Transformers-BART Model’ project on Google Cloud Platform (GCP)
Tech stack
Approach
Three components for ML model deployment
Steps:
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