Workshop / Overview

In this hands-on workshop, we will build an end-to-end AI/ML pipeline for natural language processing with BERT, TensorFlow/Keras and Amazon SageMaker.

Workshop / Outcome

Attendees will learn how to

  • Ingest data into S3 using Amazon Athena
  • 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 / Difficulty

Intermediate level

Workshop / Prerequisites

• Good internet connection and a modern web browser (preferably Chrome). 
• Working knowledge of machine learning algorithms and concepts.
• Proficient in Python programming at an intermediate level, and should be familiar with Jupyter notebooks and statistics.
• Preferably attendees also have a basic working knowledge of AWS. 

Track / Co-organizers

Chris Fregly

Principal Developer Advocate, AI and Machine Learning

Antje Barth

Developer Advocate, AI and Machine Learning, AWS

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