GCP Helicopter Racing League

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Helicopter Racing League

Overview

  • Regional league
  • Offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.

Solution Concept

  •  migrate their existing service to a new platform 
  • to expand their use of managed AI and ML services to facilitate race predictions.
  • they want to move the serving of their content, both real-time and recorded, closer to their users.

Existing Technical Environment

  •  public cloud-first company
  • core of their mission-critical applications runs on their current public cloud provider.
  • Video recording and editing is performed at the race tracks,
  •  the content is encoded and transcoded, where needed, in the cloud.
  • Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers.
  • Their race prediction services are hosted exclusively on their existing public cloud provider.[CloudML/Tensorflow/MLWorkflow]
  • Existing content is stored in an object storage service on their existing public cloud provider.[Google Storage bucket]
  • Video encoding and transcoding is performed on VMs created for each job.
  • Race predictions are performed using TensorFlow running on VMs in the current public cloud provider.[CloudML/Tensorflow]

Business Requirement

HRL’s owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:

  • Support ability to expose the predictive models to partners.[Cloud Endpoint]
  • Increase predictive capabilities during and before races:(Race result/Mechanical Failure/Crowd sentiment) [Data Ingestion/Data storage/ Processing]
  • Increase telemetry and create additional insights.
  • Measure fan engagement with new predictions.
  • Enhance global availability and quality of the broadcasts.
  • Increase the number of concurrent viewers.
  • Minimize operational complexity.
  • Ensure compliance with regulations.
  • Create a merchandising revenue stream .[Cloud Endpoint]

Technical Requirements   

  • Maintain or increase prediction throughput and accuracy.
  • Reduce viewer latency.
  • Increase transcoding performance.
  • Create real-time analytics of viewer consumption patterns and engagement.
  • Create a data mart to enable processing of large volumes of race data

Executive Statement

  •  enhanced video streams [AutoML Video Intelligence/Video Intelligence API]
  • to include predictions of events within the race (e.g., overtaking).
  •  Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.

 

=======================Our Analysis==========================

  • Streaming ==>> Data Processing
  • Predictions ==>> Machine Learning

Enterprise grade connectivity ==>>   


Holistic security: Enterprise-grade necessitates a holistic approach towards security, across products, processes, and applications.
Integration: Enterprise-grade expects tools and technologies to add to and extend existing tools, such that end users don’t have to face any disruption in their routine work.
Productivity: Enterprise-grade requires technologies to be attuned to the idea of “more work in less time,” such that users don’t feel inclined to use consumer-grade alternatives for more convenience.
Support: vendors need to support not only the internal stakeholder but also make sure the technology gels well into the larger distributed ecosystem.
Granular control: Enterprise-grade technology must offer companies deep control over policies for accessibility of content, and the ability to manage user environments suitable for specific user groups




Reference Link:
[AutoML Video Intelligence/Video Intelligence API]

[Cloud Endpoint]

[Data Ingestion/Data storage/ Processing]







services category


link



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