Hands-on exercise – building a data science environment using AWS services
In this hands-on exercise, you will create a data science environment using SageMaker with AWS CodeCommit as the source control.
Problem statement
As an ML Solutions Architect, you have been tasked with building a data science environment on AWS for the data scientists in the Equity Research department. The data scientists in the Equity Research department have several NLP problems, such as detecting the sentiment of financial phrases. Once you have created the environment for the data scientists, you also need to build a proof of concept to show the data scientists how to build and train an NLP model using the environment.
Dataset
The data scientists have indicated that they like to use the BERT model to solve sentiment analysis problems, and they plan to use the financial phrase dataset to establish some initial benchmarks for the model: https://www.kaggle.com/ankurzing/sentiment-analysis...
