AI and Machine Learning at Red Hat

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

Roles

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
H2O.ai
ProphetStor
Seldon
CognitiveScale

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 ]