Non-italicised points represent notes taken from Google’s official case study documentation, with keywords highlighted in bold. Points in italics represent additional insights and possible solutions.


  • Global sports league for helicopter racing
  • World championship and regional events
    • Need to cater for global and regional traffic
    • Points of presence in many regions—close to end users
    • CDN
  • Offers paid service to stream races with live telemetry and predictions throughout
    • Uptime important
    • Real-time analytics
  • Wish to migrate to a new platform
    • Currently on AWS or Azure—Storage Transfer Service
  • Increase use of AI/ML services
  • More serving of real time and recorded content closer to users in emerging markets

Existing Technical Environment

  • Public cloud-first company
  • Video recorded/edited at racetracks
  • Encoding and transcoding performed in the cloud
    • VMs
  • Enterprise connectivity and local compute in truck mounted DCs
    • Mobile DCs—dedicated interconnect not an option
  • Race prediction service hosted on cloud
  • Content stored in object storage
    • Migration—Storage Transfer Service
  • Video encoding/transcoding performed on VMs for each job
    • Lift/shift with Migrate for Compute Engine
    • GPUs
    • Machine types:
      • Tau T2D (option for scale-out architecture, no GPU support)
      • C2, C2D (ultra-high performance, no GPU support)
      • A2 (optimized for GPU usage)
    • Transcoder API—managed service
  • Race predictions via TensorFlow models running on VMs
    • Lift/shift with Migrate for Compute Engine
    • TPUss—Tensor Processing Unit, built for TensorFlow
    • Vertex AI

Business Requirements

  • Expand predictive capabilities
  • Reduce latency in emerging markets
  • Expose models to partners
    • Private connectivity – VPN
    • API Gateway / Apigee
  • Increase predictive capabilities (race results, mechanical failures, crowd sentiment)
    • Previous race results—batch data analysis
    • Sentiment analysis:
      • Natural Language API—predefined categories inc. sentiment analysis
      • AutoML Natural Language—custom categories
      • Vertex AI—Video Intelligence API
  • Increase telemetry and insights
  • Measure engagement with new predictions
  • Enhance global availability and quality of broadcasts
    • Higher quality video encoding—high spec VMs (Tau, GPUs)
  • Increase number of concurrent viewers
  • Minimize operational complexity
    • Managed services where possible
    • Reduce number of platforms
  • Ensure regulatory compliance
  • New revenue streams e.g. merchandise

Technical Requirements

  • Maintain/increase prediction throughput/accuracy
    • TPUs, Vertex AI
  • Reduce viewer latency
    • Content closer to viewers
    • Multi-regional GCS buckets
    • Cloud CDN
    • Global Load Balancers
    • HSL protocol—HTTP Live Streaming, Apple protocol for on-demand auto/video
  • Increase transcoding performance
    • Vertical and horizontal scaling
    • Transcoder API—batch processing
  • Real-time analytics of viewer consumption
  • Data mart—processing large volumes of race data
    • AutoML Vision
    • Vertex AI
    • BigQuery ML

Potential Solution Design

Helicopter racing league design


Graph View