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.

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

 
• 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.