github.com/sentienttechnologies/studio-go-runner@v0.0.0-20201118202441-6d21f2ced8ee/docs/slides/2018-cloud-neutral-k8s.html (about)

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     6  		<title>Studio.ml Cloud Neutral Platform - 2018</title>
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     8  		<meta name="description" content="The 2018 mission for Sentients use of StudioML">
     9  		<meta name="author" content="karl Mutch">
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    36                      <h3>Cloud Agnostic StudioML - 2018 - 2019</h3>
    37                  </section>
    38                  <section data-transition="convex">
    39                      <h5>Motivation</h5>
    40                      <br>
    41                      <h5>Ever growing list of deployment configurations and business interests</h5>
    42                      <br>
    43                      <ul style="font-size: 66%">
    44                          <li>StudioML is totally worthwhile and useful</li>
    45                          <li>StudioML has multi-cloud capability now</li>
    46                      </ul>
    47                      <br> <br>
    48                      <h4>Downside</h4>
    49                      <ul style="font-size: 66%">
    50                          <li>linear development cost for each deployment config</li>
    51                          <li>testing cost/complexity slope</li>
    52                          <li>not so adaptable/flexible to business changes</li>
    53                      </ul>
    54                  </section>
    55                  <section data-transition="convex">
    56                      <h4>One proposal for StudioML Sentient effort</h4>
    57                      <ul style="font-size: 66%">
    58                          <li>Encourage StudioML to gain increased independence, community governance for pypi for example</li>
    59                          <li>Fix https://github.com/studioml/studio/community</li>
    60                          <li>Simplify hosting for StudioML docs etc</li>
    61                      </ul>
    62                      <br> <br>
    63                      <h4>Decouple from technology choices made by/for StudioML stakeholders</h4>
    64                      <br>
    65                      <ul style="font-size: 66%">
    66                          <li>provide one broadly applicable portable deployment/technology stack</li>
    67                          <li>encourage the StudioML community to deal with use cases for individual stakeholders</li>
    68                          <li>keep future Sentient StudioML work to being cloud/deployment model agnostic</li>
    69                      </ul>
    70                  </section>
    71                  <section data-transition="convex">
    72                      <h3>Cloud Vendor Agnostic StudioML</h3>
    73                      <h5>Reference deployment</h5>
    74                      <ul style="font-size: 66%">
    75                          <li>vanilla kubernetes</li>
    76                          <li>k8s based minio s3 with persisted volume</li>
    77                          <li>rabbitMQ</li>
    78                      </ul>
    79                      <br> <br>
    80                      <ul style="font-size: 66%">
    81                          <li>Azure - acs-engine</li>
    82                          <li>AWS - kops</li>
    83                          <li>Google - GKE</li>
    84                          <li>Data center - Techtonic with matchbox</li>
    85                          <li>Laptops and Workstations - microk8s</li>
    86                      </ul>
    87                  </section>
    88                  <section data-transition="convex">
    89                      <h3>Why now</h3>
    90                      <br>
    91                      <ul style="font-size: 66%">
    92                          <li>Addition of new cloud platform use cases, both informs and challenges</li>
    93                          <li>Need to establish StudioMLs raison d'ĂȘtre for Commercial deployments, not just Research</li>
    94                      </ul>
    95                      <br>
    96                      <br>
    97                      <h3>Whats in it for the user community</h3>
    98                      <br>
    99                      <ul style="font-size: 66%">
   100                          <li>allow diverse partners to support their specific deployment use cases</li>
   101                          <li>small skill set needed for researchers to do AI at scale</li>
   102                          <li>a single reference solution, avoids make it work everywhere, saving time and $</li>
   103                          <li>private infrastructure use cases for govt etc</li>
   104                          <li>attack cost of deployment, and ownership</li>
   105                      </ul>
   106                  </section>
   107                  <section data-transition="convex">
   108                      <h3>What might it take</h3>
   109                      <br>
   110                      <ul style="font-size: 66%">
   111                          <li>PoC of k8s helm S3 minio model</li>
   112                          <li>helm deployment chart for studio-go-runner</li>
   113                          <li>rabbitMQ support for Studio.ML ecosystem, studio-go-runner</li>
   114                      </ul>
   115                      <br>
   116                      <br>
   117                      <h3>What we might need</h3>
   118                      <br>
   119                      <ul style="font-size: 66%">
   120                          <li>consider having a python StudioML maintainer</li>
   121                      </ul>
   122                  </section>
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