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Envelope-to: dr.sultan@jack.ngo
Delivery-date: Mon, 01 Jun 2020 07:03:06 -0400
Date: Mon, 1 Jun 2020 06:02:25 -0500 (CDT)
From: Canonical | Ubuntu <ubuntu-marketing@canonical.com>
Reply-To: ubuntu-marketing@canonical.com
To: dr.sultan@jack.ngo
Message-ID: <1958496670.-164270128.1591009345893.JavaMail.mktmail@sjmas05.marketo.org>
Subject: Standardising machine learning from workstation to production
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Hi=C2=A0Sultan,
In April, we talked about the landscape for Dell=E2=80=99s Data Science Wor=
kstation, which is a development enterprise-class workstation for data scie=
ntists. Continuing the initial discussion, join Dell and Canonical once aga=
in to discover the AI journey from modelling and testing on the desktop to =
production in the data centre. We will also learn about the foundation of c=
ontainers in AI - including how to harness the potential of container orche=
stration of Kubernetes in Machine Learning deployments at scale.

Join us to=C2=A0discuss:

=09- Dell workstations in AI/ML - Hear the latest info on the Ubuntu-based =
Dell Data Science Workstation platform lineup available in the DSW Guided I=
nstall Edition - Mobiles, Towers and Racks
=09- Microk8s for lightweight Kubernetes - Learn about =E2=80=9Cunder the d=
esk=E2=80=9D clustering (a.k.a. carpet clusters), and testing on a Kubernet=
es cluster
=09- Deploying and using Kubeflow on top of MicroK8s - for portability of A=
I/ML workloads with the best tools for machine learning

<http://mkto-g0030.com/K00GfEO30VH80khIZ11f0V0>

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ow:hidden; border-width:0; display:none !important;">In April, we talked ab=
out the landscape for Dell=E2=80=99s Data Science Workstation, which is a d=
evelopment enterprise-class workstation for data scientists. Continuing the=
 initial discussion, join Dell and Canonical once again to discover the AI =
journey from modelling and testing on the desktop to production in the data=
 centre. We will also learn about the foundation of containers in AI - incl=
uding how to harness the potential of container orchestration of Kubernetes=
 in Machine Learning deployments at scale.</div>
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play: inline !important;">Sultan,</span></p> </span></td>=20
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ont-family: Ubuntu, Arial, Helvetica, sans-serif; line-height:24px; font-si=
ze:15px; color:#717073; font-family:Ubuntu, Arial, Helvetica, sans-serif; "=
><p>In April, we talked about the landscape for Dell=E2=80=99s Data Science=
 Workstation, which is a development enterprise-class workstation for data =
scientists. Continuing the initial discussion, join Dell and Canonical once=
 again to discover the AI journey from modelling and testing on the desktop=
 to production in the data centre. We will also learn about the foundation =
of containers in AI - including how to harness the potential of container o=
rchestration of Kubernetes in Machine Learning deployments at scale.<br><br=
>Join us to&nbsp;discuss:<br></p>=20
<ul>=20
<li>Dell workstations in AI/ML - Hear the latest info on the Ubuntu-based D=
ell Data Science Workstation platform lineup available in the DSW Guided In=
stall Edition - Mobiles, Towers and Racks</li>=20
<li>Microk8s for lightweight Kubernetes - Learn about =E2=80=9Cunder the de=
sk=E2=80=9D clustering (a.k.a. carpet clusters), and testing on a Kubernete=
s cluster</li>=20
<li>Deploying and using Kubeflow on top of MicroK8s - for portability of AI=
/ML workloads with the best tools for machine learning</li>=20
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