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October 08

Building a Feature Store — A Machine Learning-aware Data Lake

Machine Learning starts with clean and reliable data. A feature store is a purposely built data lake for machine learning. A unified enterprise-wide feature store is a core component for reproducible machine learning experiments and production deployments.

What you will learn

In this workshop, we will strategize on architecting and building ingestion, processing, storage, and metadata capturing components of the feature lake to demonstrate how it can be used for datasets versioning, reproducible machine learning experiments, and for machine learning governance — to keep a lineage between final machine learning predictions, training pipelines, and original datasets used for training.

Who should attend

Technology Leaders, Senior Solutions Architects and Data Engineers.

Presented by Stepan Pushkarev, CTO, and Patrick McDermott, Business Development Leader, both of Provectus, including an overview of Data & ML trends, latest associated technologies and cloud solutions, delivered by Nirav Shah, AWS Solutions Architect.

Agenda

  • 8:30-9:00 Registration, Breakfast & Networking
  • 9:00-9:45 Opening Remarks: Feature Store as a Foundation for Reproducible Machine Learning
  • 9:45-10:15 Best practice: AWS Customer Use Case
  • 10:15-10:45 Coffee & Networking break
  • 10:45-11:30 Machine Learning-Aware Data Lake on AWS
  • 11:30-12:15 Model Training, Evaluation, Testing and Deployment on SageMaker and Kubeflow
  • 12:15-12:30 Q&A

Where

SFO28, Room #19.201
525 Market St, San Francisco, CA 94105

Reserve Your Seat

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