O'Reilly Book

Data Science on AWS

YouTube Videos, Meetups, Book, and Code: https://datascienceonaws.com

Workshop Description

In this hands-on workshop, we will build an end-to-end AI/ML pipeline for natural language processing with Amazon SageMaker. We will train and tune a text classifier to classify text-based product reviews using the state-of-the-art BERT model for language representation.

To build our BERT-based NLP model, we use the Amazon Customer Reviews Dataset which contains 150+ million customer reviews from Amazon.com for the 20 year period between 1995 and 2015. In particular, we train a classifier to predict the star_rating (1 is bad, 5 is good) from the review_body (free-form review text).

Learning Objectives

Attendees will learn how to do the following:

Ingest data into S3 using Amazon Athena and the Parquet data format
Visualize data with pandas, matplotlib on SageMaker notebooks
Run data bias analysis with SageMaker Clarify
Perform feature engineering on a raw dataset using Scikit-Learn and SageMaker Processing Jobs
Store and share features using SageMaker Feature Store
Train and evaluate a custom BERT model using TensorFlow, Keras, and SageMaker Training Jobs
Evaluate the model using SageMaker Processing Jobs
Track model artifacts using Amazon SageMaker ML Lineage Tracking
Run model bias and explainability analysis with SageMaker Clarify
Register and version models using SageMaker Model Registry
Deploy a model to a REST Inference Endpoint using SageMaker Endpoints
Automate ML workflow steps by building end-to-end model pipelines using SageMaker Pipelines

Workshop Cost

This workshop is FREE, but would otherwise cost <25 USD.

Workshop Agenda

Workshop Paths

Quick Start (All-In-One Workshop Path)

Workshop Contributors

Workshop Instructions

Note: This workshop will create an ephemeral AWS acccount for each attendee. This ephemeral account is not accessible after the workshop. You can, of course, clone this GitHub repo and reproduce the entire workshop in your own AWS Account.

0. Logout of All AWS Consoles Across All Browser Tabs

If you do not logout of existing AWS Consoles, things will not work properly.

Please logout of all AWS Console sessions in all browser tabs.

1. Login to the Workshop Portal (aka Event Engine).

THE LINK WILL BE PROVIDED TO YOU

2. Login to the AWS Console

Take the defaults and click on Open AWS Console. This will open AWS Console in a new browser tab.

If you see this message, you need to logout from any previously used AWS accounts.

Please logout of all AWS Console sessions in all browser tabs.

Double-check that your account name is similar to TeamRole/MasterKey as follows:

If not, please logout of your AWS Console in all browser tabs and re-run the steps above!

3. Launch SageMaker Studio

Open the AWS Management Console

In the AWS Console search bar, type SageMaker and select Amazon SageMaker to open the service console.

4. Launch a New Terminal within Studio

Click File > New > Terminal to launch a terminal in your Jupyter instance.

5. Clone this GitHub Repo in the Terminal

Within the Terminal, run the following:

cd ~ && git clone https://github.com/data-science-on-aws/workshop

If you see an error like the following, just re-run the command again until it works:

fatal: Unable to create '/home/sagemaker-user/workshop/.git/index.lock': File exists.

Another git process seems to be running in this repository, e.g.
an editor opened by 'git commit'. Please make sure all processes
are terminated then try again. If it still fails, a git process
may have crashed in this repository earlier:
remove the file manually to continue.

Note: This is not a fatal error ^^ above ^^. Just re-run the command again until it works.

6. Start the Workshop!

Navigate to 00_quickstart/ in SageMaker Studio and start the workshop!

You may need to refresh your browser if you don't see the new workshop/ directory.

dts3/workshop