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Envelope-to: dr.sultan@jack.ngo
Delivery-date: Wed, 16 Jun 2021 06:17:27 -0400
Date: Wed, 16 Jun 2021 05:16:45 -0500 (CDT)
From: Canonical | Ubuntu <ubuntu-marketing@canonical.com>
Reply-To: ubuntu-marketing@canonical.com
To: dr.sultan@jack.ngo
Message-ID: <40198198.400501125.1623838605998@sjmktmail-batch1h.marketo.org>
Subject: Enterprise MLOps in hybrid-cloud scenarios
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Hi=C2=A0Sultan,
The need for efficient allocation of compute resources and planning of util=
ization capacity is fast becoming a necessity in many Enterprise Machine Le=
arning Operation endeavors. Optimizing resource allocation, from both a cos=
t and technical perspective, is driving many organizations to strongly cons=
ider a hybrid-cloud infrastructure setup.

Architectural best practices that have emerged in recent times around ML wo=
rkflow pipelines, cloud-agnostic model deployment and serving, feature stor=
es, data versioning and more, make it easy for companies looking in this di=
rection to bootstrap and get up and running.

In this webinar, we will cover:
1. How to effectively bring your models to production across clouds
2. How to make the best use of feature stores
3. How to use Kubeflow Pipelines with a feature store
4. How to use Apache Hudi to unify historical and new data
5. How to use Kubeflow with Apache Spark operator
6. How to leverage model-driven operators to deploy and manage your MLOps s=
tack
7. Storage agnostic best practices (s3, gs, az storage) in the public cloud=
 and (ceph) on-prem

<http://mkto-g0030.com/MDY2LUVPVi0zMzUAAAF9s0tyrMtJfmWjQD1_q7YqiIUOGbr0f1D=
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The need for efficient allocation of compute resources and planning of util=
ization capacity is fast becoming a necessity in many Enterprise Machine Le=
arning Operation endeavors. Optimizing resource allocation, from both a cos=
t and technical perspective, is driving many organizations to strongly cons=
ider a hybrid-cloud infrastructure setup.</div>
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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>The need for efficient allocation of compute resources and planning of =
utilization capacity is fast becoming a necessity in many Enterprise Machin=
e Learning Operation endeavors. Optimizing resource allocation, from both a=
 cost and technical perspective, is driving many organizations to strongly =
consider a hybrid-cloud infrastructure setup.</p> <p>Architectural best pra=
ctices that have emerged in recent times around ML workflow pipelines, clou=
d-agnostic model deployment and serving, feature stores, data versioning an=
d more, make it easy for companies looking in this direction to bootstrap a=
nd get up and running.</p> <p>In this webinar, we will cover:<br>1. How to =
effectively bring your models to production across clouds<br>2. How to make=
 the best use of feature stores<br>3. How to use Kubeflow Pipelines with a =
feature store<br>4. How to use Apache Hudi to unify historical and new data=
<br>5. How to use Kubeflow with Apache Spark operator<br>6. How to leverage=
 model-driven operators to deploy and manage your MLOps stack<br>7. Storage=
 agnostic best practices (s3, gs, az storage) in the public cloud and (ceph=
) on-prem</p> <p><br><br></p> <p><a href=3D
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