Hands-on exercise – building a data science environment using AWS services
The primary goal of this lab is to offer practical, hands-on experience with the various SageMaker tools. Once you are familiar with the core functionality in this lab, you should independently explore other features such as Code Editor and RStudio.
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 description
The data scientists have indicated that they like to use the BERT model to solve sentiment analysis problems, and they plan...
