Red Hat Dallas Emerging Tech Summit · 2020. 1. 10. · RED HAT DALLAS EMERGING TECH SUMMIT - DEC 5, 2019 federate models train events databases file, object storage management web

Red Hat Dallas Emerging Tech Summit

AI and ML on Kubernetes William Benton • Engineering Manager and Senior Principal Engineer

December 5, 2019

@willb

What is machine learning?

RED HAT DALLAS EMERGING TECH SUMMIT - DEC 5, 2019

feature engineering

model training

and tuningmodel

validationmodel

deploymentmonitoring,

validationdata

collection and cleaning

codifying problem

and metrics

codifying problem

and metrics


codifying problem

and metrics

feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics


codifying problem

and metrics

feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics


codifying problem

and metrics

data collection

and cleaning

feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics


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codifying problem

and metrics

data collection

and cleaning

feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics


feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics

codifying problem

and metrics

feature engineering

data collection

and cleaning


feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics

codifying problem

and metrics

feature engineering

data collection

and cleaning


feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics

codifying problem

and metrics

feature engineering

data collection

and cleaning


feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics

codifying problem

and metricsfeature

engineering

model training

and tuning

data collection

and cleaning



feature engineering

model training

and tuningmodel

validationmodel


validationdata


codifying problem

and metrics

data collection

and cleaning

codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation


feature engineering

model training

and tuningmodel

validationdata


model deployment

monitoring, validation

codifying problem

and metrics

data collection

and cleaning

codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation

model deployment



feature engineering

model training

and tuning

data collection

and cleaningmodel

validationmodel


validationcodifying problem

and metrics

codifying problem

and metrics

feature engineering

model training

and tuning

model validation

data collection

and cleaning

model deployment



feature engineering

model training

and tuning

data collection

and cleaningmodel

validationmodel



and metrics

codifying problem

and metrics

feature engineering

model training

and tuning

model validation

data collection

and cleaning

model deployment







data as the foundation

@willb

Why “on Kubernetes?”


codifying problem

and metricsfeature

engineeringmodel

training and tuning

data collection

and cleaningmodel

validationmodel



and metricsfeature

engineering

model training

and tuning

model validation

data collection

and cleaning

model deployment



feature engineering

model training

and tuning

model validation

model deployment


data collection

and cleaning

codifying problem

and metrics

feature engineering

model training

and tuning

model deployment














OpenShift is enterprise Kubernetes with a great developer experience.


declarative deployments; resource managementfor apps and compute



efficient isolation, secure by default




efficient isolation, secure by default


workflows to accelerate discovery


@willb

Some common concerns for AI/ML systems




0 0 0 1 1 0 1 0 1 0

0 0 1 0 0 0 1 1 0 0

1 0 1 1 0 1 0 0 0 0

0 0 0 0 0 0 1 1 0 1

0 1 0 0 1 0 0 1 0 0

1 0 0 0 0 1 0 1 1 0

0 0 1 0 1 0 1 0 0 0

0 1 0 0 0 1 0 0 1 1

0 0 0 0 1 0 0 1 0 1

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0.13 0.13

0.06 0.07

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0.17 0.11

0.11 0.09

0.04 0.18

0.13 0.04

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0.14 0.03

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more storage

sensitive data

more CPUsbetter GPUs






OK!

OK!











mA


mA


mA

distribution of input data?

distribution of predictions?

distribution of number of multiplications while scoring?

(joint)


@willb

Intelligent applications are machine learning systems


federate

trainmodels

events

databases

file, object storage

management

web and mobile

reporting

developer UItransform

transform

transform

archive

codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation

data collection

and cleaningmodel


validation


data engineers

federate

events

databases


transform

transform

transform

archive


data scientists

federate

trainmodels


transform

transform


application developers

federate

trainmodels management

web and mobile

reporting


transform

transform

archive


data scientists

application developersdata engineers

federate

trainmodels

events

databases


management

web and mobile

reporting


transform

transform

archive


data scientists

application developersdata engineers

federate

trainmodels

events

databases


management

web and mobile

reporting


transform

transform

archive

machine learning engineers

@willb

Managing compute and data in a shared discovery environment







OpenShift



@willb

There’s no one-size-fits-all architecture for AI/ML


OpenShift


OpenShift


OpenShift


OpenShift

OpenShift OpenShift

OpenShift


@willb

From developer experience to data scientist experience(s)



codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation

data collection

and cleaningmodel


validation


codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation

data collection

and cleaningmodel


validation


codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation

data collection

and cleaningmodel


validation



@willb

Conclusions


codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation

data collection

and cleaningmodel


validation


codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation

data collection

and cleaningmodel


validation


codifying problem

and metricsfeature

engineeringmodel

training and tuning

model validation

data collection

and cleaningmodel


validation

Documents

Red Hat Dallas Emerging Tech Summit · 2020. 1. 10. · RED HAT DALLAS EMERGING TECH SUMMIT - DEC 5, 2019 federate models train events databases file, object storage management web