machine learning and discovery with kubernetes · machine learning and discovery with kubernetes...

Machine learning and discovery with KubernetesWilliam Benton • @willb • willb@redhat.com

What do machine learning workflows look like?

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data collection and cleaning

codifying problem and metrics

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feature engineering

model training and tuning

data collection and cleaning

@willb #SEMLA19

feature engineering

model training and tuning

data collection and cleaning

@willb #SEMLA19

feature engineering

model training and tuning

model validation

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feature engineering

model training and tuning

model validation

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model validation

model deployment

monitoring and validation

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model validation

model deployment

monitoring and validation

@willb #SEMLA19

feature engineering

model training and tuning

model validation

model deployment

monitoring and validation

data collection and cleaning

codifying problem and metrics

@willb #SEMLA19

feature engineering

model training and tuning

model validation

model deployment

monitoring and validation

data collection and cleaning

codifying problem and metrics

defining types and interfaces prototyping

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feature engineering

model training and tuning

model validation

model deployment

monitoring and validation

data collection and cleaning

codifying problem and metrics

unit, behavioral, and integration testing

formal verification

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feature engineering

model training and tuning

model validation

model deployment

monitoring and validation

data collection and cleaning

codifying problem and metrics

deployment monitoring

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What’s a container?

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%

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% pip install numpy

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pip install numpy

/usr/bin/pip executable

arguments

virtual memory

file handles

/root filesystem

environment LANG=en_US USER=willb ...

process table

network routes

@willb #SEMLA19

pip install numpy

/usr/bin/pip executable

arguments

virtual memory

file handles

/root filesystem

environment LANG=en_US USER=willb ...

process table

network routes

@willb #SEMLA19

pip install numpy

/usr/bin/pip executable

arguments

virtual memory

file handles

/root filesystem

environment LANG=en_US USER=willb ...

process table

network routes

@willb #SEMLA19

pip install numpy

/usr/bin/pip executable

arguments

virtual memory

file handles

/root filesystem

environment LANG=en_US USER=willb ...

process table

network routes

@willb #SEMLA19

pip install numpy

/usr/bin/pip executable

arguments

virtual memory

file handles

/root filesystem

environment LANG=en_US USER=willb ...

process table

network routes

@willb #SEMLA19

pip install numpy

/usr/bin/pip executable

arguments

virtual memory

file handles

/var/lib/envs/mainroot filesystem

environment LANG=en_US USER=willb ...

process table

network routes

@willb #SEMLA19

pip install numpy

/usr/bin/pip executable

arguments

virtual memory

file handles

/var/lib/envs/mainroot filesystem

environment LANG=en_US USER=willb ...

process table

network routes

100MAXIMUM

km / h

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Immutable images

base image

configuration and installation recipes

user application code

979229b9

33721112 e8cae4f6 2bb6ab16 a8296f7e

a6afd91e 6b8cad3e

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Stateless microservices

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Stateless microservices

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Stateless microservices

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Stateless microservices

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Stateless microservices

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Stateless microservices

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Stateless microservices

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Stateless microservices

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Declarative app configuration

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Integration and deployment

OK!

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Integration and deployment

OK!base image

configuration and installation recipes

application code

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Integration and deployment

OK!base image

configuration and installation recipes

application code

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Integration and deployment

base image

configuration and installation recipes

application code

What containers offerdata scientists

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OK!

OK!

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No friction: mybinder.org

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More flexible: source-to-image

%

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More flexible: source-to-image

%

https://github.com/openshift/source-to-image

builder image application image

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mA

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mA

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mA

distribution of input data?

distribution of predictions?

distribution of acyclic paths taken through scoring code?

(joint)

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Where from here?

@willb #SEMLA19data scientists

application developersdata engineers

federate

trainmodels

events

databases

file, object storage

management

web and mobile

reporting

developer UItransform

transform

transform

archive

@willb #SEMLA19data scientists

application developersdata engineers

federate

trainmodels

events

databases

file, object storage

management

web and mobile

reporting

developer UItransform

transform

transform

archive

machine learning engineers

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radanalytics.io

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opendatahub.io

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Kubeflow

What did we talk about today?

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willb@redhat.com • @willb https://chapeau.freevariable.com

THANKS