TDWI San Diego Conference

T6A Hands-on: Transform Data into Operational Insights with AWSNEW!

August 20, 2019

8:00 am - 11:15 am

Duration: Half Day Course

Prerequisite: None

The first step towards smarter business processes is collecting data. The next steps include preparing the data for analysis, turning that analysis into insight, and operationalizing those insights. In this hands-on course, Amazon Web Services provides cloud-managed services for the end-to-end use case of developing machine learning models and utilizing them at the edge and for batch analytics. Forecasting, anomaly detection, defect analysis, and predictive maintenance are key use cases powered by industrial data. Join Ryan Burke (Solutions Architect, AWS) for a live tutorial on how to build a reference architecture for industrial IoT data and turn data into insights with machine learning tools.

You Will Learn

  • How to generate and ingest data into the AWS cloud using industrial machine data as use case
  • How to pipeline (ingest, cleanse, enrich, prepare) industrial data for analysis
  • How to make IoT insights on industrial data with statistical queries
  • How to load prepared industrial data into Jupyter Notebooks and leverage the AWS cloud to rapidly build and test models
  • How to operationalize models as on-demand resources in the cloud and at the edge

Geared To

  • Architects and data engineers tasked with pipelining raw and/or siloed data into actionable intelligence
  • Engineers learning how to wrangle machine data in the cloud to automate a process with machine learning capabilities
  • IT and OT team leaders interested in learning how IoT can converge their respective expertise
  • Data analysts interested in learning how an IoT data pipeline is created to enable analytical processes

Instructions will be emailed to registrants prior to the event to prepare your laptop BEFORE the conference. There is no time allotted in class for laptop preparation.

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TDWI San Diego

San Diego Hotel & Marina
San Diego, CA
August 18–23