Tutorials / Extend Sagemaker with Location Intelligence
Last Updated: July 31, 2020
Extend the capability of SageMaker with HERE Location Services
Duration is 5 min
How do you optimize Machine Learning? Start HERE
Location intelligence that deepens Machine Learning
Rising customer expectations have put unprecedented pressure on businesses. In response, forward-thinking businesses use Machine Learning to tap into the big data needed to predict what customers want, when they want it, and where they want to get it. To optimize Machine Learning organizations must be able to improve location data within Machine Learning datasets, to avoid incomplete location data and missing datasets. Those are just a few of the many reasons location intelligence has become a critical tool for organizations around the world— regardless of industry.
Explore how location intelligence combined with machine Learning dramatically increase your ability to respond to the needs of your organization.
Benefits of integrating HERE location awareness technology with SageMaker
Increases business insights with deeper visualization of all aspects of business
Enables real-time tracking of assets, devices, products and people in the field
Creates visibility of entire shipments down to individual SKUs
Facilitates ad-hoc queries for any neighborhood, city, territory and region
Delivers Intelligent location data for better decision-making
Provides unmatched flexibility with cloud-based services
Adds valuable context through geospatial data
Drives business value through differentiated services like route optimization, sequencing of waypoints and more
Provides map visualization, geocoding, optimized routing and more
Start the tutorial
This tutorial walks you through the steps required to integrate your Machine Learning (ML) data pipeline with HERE Location Services. This tutorial will use Amazon SageMaker to manage the ML workflow.
What you’ll learn
How to leverage HERE Location Services to enrich an ML dataset with additional location information
How to integrate the ML dataset with Amazon SageMaker
What you’ll build
AWS Lambda function that will call HERE Location Services and use the returned data to update the ML dataset with additional location data.
Assumptions & Prerequisites
A familiarity with cloud computing and AWS products
In this tutorial, we created a SageMaker Jupyter Notebook that utilized HERE Location Services to add valuable location data to a fictitious ‘incidents’ dataset during the “fetch” phase of the Machine Learning model. The additional location data will allow Machine Learning models to gather greater insights from the data once it is cleaned and prepared for training & evaluating.