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
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
Moroccan Darija Wikipedia: Basics of Natural Language Processing for a Low-Resource Language
With Ihsane Gryech, Khalil Mrini, Abdelhak Mahmoudi, Anass Sedrati & Imane Khaouja16:00-22:00 September 04Online - Socio
Fundamentals of Accelerated Data Science with RAPIDS [NVIDIA]
With Manal Jalloul10:00-14:00 September 04Online - Socio