Machine learning and discovery with Kubernetes William Benton • @willb • willb@redhat.com
What do machine learning workflows look like?
@willb #SEMLA19
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codifying problem data collection and metrics and cleaning @willb #SEMLA19
data collection feature model training and cleaning engineering and tuning @willb #SEMLA19
data collection feature model training and cleaning engineering and tuning @willb #SEMLA19
feature model training model engineering and tuning validation @willb #SEMLA19
feature model training model engineering and tuning validation @willb #SEMLA19
model model monitoring and validation deployment validation @willb #SEMLA19
model model monitoring and validation deployment validation @willb #SEMLA19
codifying problem data collection feature model training model model monitoring and and metrics and cleaning engineering and tuning validation deployment validation @willb #SEMLA19
codifying problem data collection feature model training model model monitoring and and metrics and cleaning engineering and tuning validation deployment validation defining types prototyping and interfaces @willb #SEMLA19
codifying problem data collection feature model training model model monitoring and and metrics and cleaning engineering and tuning validation deployment validation unit, behavioral, and formal integration testing verification @willb #SEMLA19
codifying problem data collection feature model training model model monitoring and and metrics and cleaning engineering and tuning validation deployment validation deployment monitoring @willb #SEMLA19
@willb #SEMLA19
@willb #SEMLA19
What’s a container?
% @willb #SEMLA19
% pip install numpy @willb #SEMLA19
executable /usr/bin/pip arguments pip install numpy environment LANG=en_US USER=willb ... virtual memory file handles root filesystem / process table network routes @willb #SEMLA19
executable /usr/bin/pip arguments pip install numpy environment LANG=en_US USER=willb ... virtual memory file handles root filesystem / process table network routes @willb #SEMLA19
executable /usr/bin/pip arguments pip install numpy environment LANG=en_US USER=willb ... virtual memory file handles root filesystem / process table network routes @willb #SEMLA19
executable /usr/bin/pip arguments pip install numpy environment LANG=en_US USER=willb ... virtual memory file handles root filesystem / process table network routes @willb #SEMLA19
executable /usr/bin/pip arguments pip install numpy environment LANG=en_US USER=willb ... virtual memory file handles root filesystem / process table network routes @willb #SEMLA19
executable /usr/bin/pip arguments pip install numpy environment LANG=en_US USER=willb ... virtual memory file handles root filesystem /var/lib/envs/main process table network routes @willb #SEMLA19
executable /usr/bin/pip arguments pip install numpy MAXIMUM environment LANG=en_US USER=willb ... 100 virtual memory file handles root filesystem /var/lib/envs/main process table km / h network routes @willb #SEMLA19
Immutable images a6afd91e user application code 6b8cad3e 33721112 configuration and e8cae4f6 2bb6ab16 installation recipes a8296f7e base image 979229b9 @willb #SEMLA19
Stateless microservices @willb #SEMLA19
Stateless microservices @willb #SEMLA19
Stateless microservices @willb #SEMLA19
Stateless microservices @willb #SEMLA19
Stateless microservices @willb #SEMLA19
Stateless microservices @willb #SEMLA19
Stateless microservices @willb #SEMLA19
Stateless microservices @willb #SEMLA19
Declarative app configuration @willb #SEMLA19
Integration and deployment OK! @willb #SEMLA19
Integration and deployment application code OK! configuration and installation recipes base image @willb #SEMLA19
Integration and deployment application code OK! configuration and installation recipes base image @willb #SEMLA19
Integration and deployment application code configuration and installation recipes base image @willb #SEMLA19
What containers offer data scientists
@willb #SEMLA19
@willb #SEMLA19
O K ! O K ! @willb #SEMLA19
No friction: mybinder.org @willb #SEMLA19
More flexible: source-to-image % @willb #SEMLA19
More flexible: source-to-image % builder image application image https://github.com/openshift/source-to-image @willb #SEMLA19
@willb #SEMLA19
@willb #SEMLA19
@willb #SEMLA19
m A @willb #SEMLA19
m A @willb #SEMLA19
(joint) distribution of input data? distribution of predictions? m A distribution of acyclic paths taken through scoring code? @willb #SEMLA19
@willb #SEMLA19
Where from here?
application developers data engineers web and transform developer UI events mobile transform federate databases archive file, object transform reporting storage train management models data scientists @willb #SEMLA19
application developers data engineers web and transform developer UI events mobile transform federate databases archive file, object transform reporting storage train management models machine learning engineers data scientists @willb #SEMLA19
radanalytics.io @willb #SEMLA19
opendatahub.io @willb #SEMLA19
Kubeflow @willb #SEMLA19
What did we talk about today?
@willb #SEMLA19
@willb #SEMLA19
@willb #SEMLA19
@willb #SEMLA19
THANKS willb@redhat.com • @willb https://chapeau.freevariable.com @willb #SEMLA19
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