In July 2021, AWS and Hugging Face announced collaboration to make Hugging Face a first party framework within SageMaker. Earlier, you had to use PyTorch container and install packages manually to do this. With the new Hugging Face Deep Learning Containers (DLC) availabe in Amazon SageMaker, the process of training and deploying models is greatly simplified.

In this post, we will go through a high level overview of Hugging Face Transformers library before looking at how to use the newly announced Hugging Face DLCs within Sagemaker.

In the previous post we saw how to scrape raw data from a content rich webpage. In this post, we will explore how to process that raw data and use Machine Learning tools to predict the playing role of a cricket player just based on his career statistics.

In this project, we will apply Machine Learning techniques to predict whether a particular cricket player is a batsman or bowler based on his career stats. First we will use Deep Neural Networks (DNN) model and later compare the results with a simpler classifier algorithm such as Random Forest Classifier.