AI and Machine Learning at Red Hat

This talk has been presented by Sophie Watson and William Benton.


  • Data Engineers: Process and transform large data into a database
  • Data Scientists: Design models to understand the data and produce a result
  • Application Developers: Use the tools provided by the data engineers and the results from the models by Data Scientists into something everybody can understand and use.
  • Machine Learning engineers: Train and work with models.

Components in a Machine Learning System

  • Configuration
  • Data Collection
  • Feature extraction
  • Data verification
  • Machine resource management
  • Model
  • Analysis tools
  • Process management
  • Serving infrastructure
  • Monitoring

From “Hidden Technical Debts in Machine Learning system” NIPS 2015.

Red Hat Partners

  • PerceptiLabs
  • NVidia
  • ProphetStor
  • Seldon
  • CognitiveScale

More in

Lifecycle in Machine Learning

1.- Codifying problem and metrics 2.- Data collection and cleaning 3.- Feature engineering 4.- Model training and tuning 5.- Model validation 6.- Model deployment 7.- Monitoring, validation

[ Machine Learning ]