NLP Pipeline with BERT using Amazon SageMaker Pipelines

This repository contains the code and materials for a series of labs focusing on building, training, and deploying Natural Language Processing (NLP) pipelines using BERT and Amazon SageMaker Pipelines.

Labs Overview

1. Feature Engineering and Feature Store

In this lab, you will learn how to transform a raw text dataset into machine learning features and efficiently store these features in the Amazon SageMaker Feature Store. This step is crucial for preparing your data for NLP tasks.

2. Train, Debug, and Profile a Machine Learning Model

This lab dives into the fine-tuning, debugging, and profiling of a pre-trained BERT model. You will gain insights into optimizing your model for specific NLP tasks and understand its performance characteristics.

3. Deploy End-To-End Machine Learning Pipelines

In this lab, you will learn how to orchestrate end-to-end ML workflows. You'll also explore tracking model lineage and artifacts, ensuring a streamlined deployment process for your NLP models.