Day 10 - GCP ML

You work in a major institution. The Management has decided to rapidly launch a service, as the Government has created a series of “first home” facilities for the younger population. The goal is to carry out the automatic management of the required documents (certificates, origin documents, legal information) so that the practice can be built and verified automatically using the data and documents provided by customers and can be managed in a short time and with the minimum contribution of the scarce specialized personnel. Which of these GCP services can you use?

• Dialogflow

• Document AI

• Cloud Natural Language API

• AutoML

You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. It can be classified as can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. How should you configure the pipeline?

• Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API

• Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.

• Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.

• Create a TensorFlow model using Google’s BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.

This processor applies advanced machine learning technologies to extract key-value pairs, checkboxes and tables from documents in over 200+ languages. This processor also leverages deep learning models to extract 11 generic entities that are common in various document types.

• Document OCR (Optical Character Recognition)

• Form Parser

• Intelligent Document Quality Processor

• Contract parser

Your employer is a major retailer. You want to utilise ML to project future sales using data from the previous 10 years' worth of sales. You want to quickly experiment with all the information at your disposal. How should your model for the sales forecast be created and trained?

• Convert the data into CSV format and create a regression model on AutoML Tables.

• Convert the data into TFRecords and create an RNN model on TensorFlow on AI Platform Notebooks

• Convert and refactor the data into CSV format and use the built-in XGBoost algorithm on AI Platform Training

• Load data into BigQuery and use the CLASSIFICATION model type on BigQuery ML

Your team has been tasked with creating an ML solution in Google Cloud to classify custom support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

• Use the Natural Language API to classify support requests.

• Use AutoML Natural Language to build the support requests classifier.

• Use an established text classification model on AI Platform to perform transfer learning by training the last layer

• Use an established text classification model on AI Platform as-is to classify support requests.