Merge pull request #236 from postgresml/montana/welcom

montanalow · web-flow · commit 57ebe6145085 · 2022-08-22T09:30:29.000-07:00
tweaks
diff --git a/pgml-dashboard/notebooks/fixtures/notebooks.yml b/pgml-dashboard/notebooks/fixtures/notebooks.yml
@@ -57,36 +57,57 @@
   fields:
     notebook: 0
     cell_type: 1
-    contents: "## Welcome!\n\nHi there. Welcome to what we hope is the future of machine
-      learning!\n\nPostgresML is an end-to-end system for training and deploying real
-      time machine learning models. It handles data versioning, model training, ranking,
-      \nand safe production release. This dashboard gives an overview of what's happening
-      in the system and also helps build and deploy experiments. The notebooks,\none
-      of which you're reading right this moment, are our take on what ML notebooks
-      could be when used with a real time data store like PostgreSQL.\n\n\n\n\n###
-      Notebooks\n\nOur Notebooks are similar to Jupyter Notebooks, which you might
-      be familiar with already. On the bottom of the page, you will find a text editor
-      which is used to create new cells. Each cell can contain either Markdown which
-      is just text really, and SQL which can be executed directly on this PostgresML
-      instance.\n\nEach cell has a little menu in the top right corner, allowing you
-      to (re)run it (if it's SQL), edit it, and delete it.\n\n\nLet me give you an
-      example. The next cell (cell #2) will be a SQL cell which will execute a simple
-      query."
-    rendering: "<article class=\"markdown-body\"><h2>Welcome!</h2>\n<p>Hi there. Welcome
-      to what we hope is the future of machine learning!</p>\n<p>PostgresML is an
-      end-to-end system for training and deploying real time machine learning models.
-      It handles data versioning, model training, ranking, \nand safe production release.
-      This dashboard gives an overview of what's happening in the system and also
-      helps build and deploy experiments. The notebooks,\none of which you're reading
-      right this moment, are our take on what ML notebooks could be when used with
-      a real time data store like PostgreSQL.</p>\n<h3>Notebooks</h3>\n<p>Our Notebooks
-      are similar to Jupyter Notebooks, which you might be familiar with already.
-      On the bottom of the page, you will find a text editor which is used to create
-      new cells. Each cell can contain either Markdown which is just text really,
-      and SQL which can be executed directly on this PostgresML instance.</p>\n<p>Each
-      cell has a little menu in the top right corner, allowing you to (re)run it (if
-      it's SQL), edit it, and delete it.</p>\n<p>Let me give you an example. The next
-      cell (cell #2) will be a SQL cell which will execute a simple query.</p></article>"
+    contents: '## Welcome!
+
+
+      You''re set up and running on PostgresML! This is an end-to-end system for training
+      and deploying real time machine learning models. It handles data versioning,
+      model training and validation, and safe production release. This dashboard web
+      app will give you an overview of what''s happening in the system and also helps
+      build and deploy projects. You can use notebooks like this one to interact with
+      your database in real time and organize your SQL while documenting your code.
+
+
+
+      ### Notebooks
+
+
+      These notebooks are similar to Jupyter Notebooks, which you might be familiar
+      with already. On the bottom of the page, you will find a text editor which is
+      used to create new cells. Each cell can contain either Markdown which is just
+      text really, and SQL which will be executed directly by your Postgres database
+      server.
+
+
+      Each cell has a little menu in the top right corner, allowing you to (re)run
+      it (if it''s SQL), edit it, and delete it.
+
+
+
+      Let me give you an example. The next cell (cell #2) will be a SQL cell which
+      will execute a simple query.'
+    rendering: '<article class="markdown-body"><h2>Welcome!</h2>
+
+      <p>You''re set up and running on PostgresML! This is an end-to-end system for
+      training and deploying real time machine learning models. It handles data versioning,
+      model training and validation, and safe production release. This dashboard web
+      app will give you an overview of what''s happening in the system and also helps
+      build and deploy projects. You can use notebooks like this one to interact with
+      your database in real time and organize your SQL while documenting your code.</p>
+
+      <h3>Notebooks</h3>
+
+      <p>These notebooks are similar to Jupyter Notebooks, which you might be familiar
+      with already. On the bottom of the page, you will find a text editor which is
+      used to create new cells. Each cell can contain either Markdown which is just
+      text really, and SQL which will be executed directly by your Postgres database
+      server.</p>
+
+      <p>Each cell has a little menu in the top right corner, allowing you to (re)run
+      it (if it''s SQL), edit it, and delete it.</p>
+
+      <p>Let me give you an example. The next cell (cell #2) will be a SQL cell which
+      will execute a simple query.</p></article>'
     execution_time: null
     cell_number: 1
     version: 1
@@ -97,11 +118,8 @@
     notebook: 0
     cell_type: 3
     contents: SELECT random();
-    rendering: "<div class=\"markdown-body\">\n<table>\n  <thead>\n    <tr>\n      \n
-      \     <td><strong>random</strong></td>\n      \n    </tr>\n  </thead>\n  <tbody>\n
-      \   \n    <tr>\n      \n      <td>0.6822832295608556</td>\n      \n    </tr>\n
-      \   \n  </tbody>\n</table>\n</div>\n"
-    execution_time: '00:00:00.000654'
+    rendering: null
+    execution_time: null
     cell_number: 2
     version: 1
     deleted_at: null
@@ -110,62 +128,34 @@
   fields:
     notebook: 0
     cell_type: 1
-    contents: 'I just asked Postgres to give me a random number. Pretty simple query,
+    contents: "We just asked Postgres to return a random number. Pretty simple query,
       but it demonstrates the notebook functionality pretty well. You can see that
-      the result of `random()` is currently `0.6822832295608556`. On the bottom right
-      corner, you can see that it took `0:00:00.000654` or 0 hours, 0 minutes, 0 seconds
-      and only 00006ns, which I believe is 0.6ms, for Postgres to run this query for
-      us. This runtime is good to know, because you''ll be able to benchmark some
-      of PostgresML functionality, including the models we provide, right here in
-      these notebooks.
-
-
-      Try rerunning the cell again by clicking the "play" button in the top right
-      corner. You''ll see that the random number will change. Rerunning is a real
-      time operation and Postgres will give you a different random number every time
-      (otherwise it wouldn''t be random).
-
-
-      #### Editing a cell
-
-
-      You can edit a cell at any time, including SQL cells which will then run the
-      new query immediately.
-
-
-      #### Deleting a cell
-
-
-      Deleting a cell is pretty easy: just click on the delete button in the top right
-      corner. You''ll have 10 seconds to undo the delete if you so desire; we wouldn''t
-      want you to lose your work because of an accidental click.
-
-
-      #### Shortcuts
-
-
-      The text editor supports the following helpful shortcuts:
-
-
-
-      | Shortcut |             Description               |
-
-      -----------| --------------------------------------
-
-      | `Cmd-/` or `Ctrl-/` | Comment out SQL code.      |
-
-      | `Cmd-Enter` or `Ctrl-Enter` | Save/create a cell.|
-
-
-      By the way, this was a Markdown table, you can make those here as well.'
-    rendering: '<article class="markdown-body"><p>I just asked Postgres to give me
+      the result of `random()` is a float between 0 and 1. On the bottom right corner,
+      you can see that it took `0:00:00.000654` or 0 hours, 0 minutes, 0 seconds and
+      only 654ns, or 0.6ms. This run time is good to keep an eye on. It will help
+      build an intuition for how fast Postgres really is, and how certain operations
+      scale as the data grows. You'll be able to see how long \n\nTry rerunning the
+      cell again by clicking the \"play\" button in the top right corner. You'll see
+      that the random number will change. Rerunning is a real time operation and Postgres
+      will give you a different random number every time (otherwise it wouldn't be
+      random).\n\n#### Editing a cell\n\nYou can edit a cell at any time, including
+      SQL cells which will then run the new query immediately.\n\n#### Deleting a
+      cell\n\nDeleting a cell is pretty easy: just click on the delete button in the
+      top right corner. You'll have 10 seconds to undo the delete if you so desire;
+      we wouldn't want you to lose your work because of an accidental click.\n\n####
+      Shortcuts\n\nThe text editor supports the following helpful shortcuts:\n\n\n|
+      Shortcut |             Description               |\n-----------| --------------------------------------\n|
+      `Cmd-/` or `Ctrl-/` | Comment out SQL code.      |\n| `Cmd-Enter` or `Ctrl-Enter`
+      | Save/create a cell.|\n\nBy the way, this was a Markdown table, you can make
+      those here as well."
+    rendering: '<article class="markdown-body"><p>We just asked Postgres to return
       a random number. Pretty simple query, but it demonstrates the notebook functionality
-      pretty well. You can see that the result of <code>random()</code> is currently
-      <code>0.6822832295608556</code>. On the bottom right corner, you can see that
-      it took <code>0:00:00.000654</code> or 0 hours, 0 minutes, 0 seconds and only
-      00006ns, which I believe is 0.6ms, for Postgres to run this query for us. This
-      runtime is good to know, because you''ll be able to benchmark some of PostgresML
-      functionality, including the models we provide, right here in these notebooks.</p>
+      pretty well. You can see that the result of <code>random()</code> is a float
+      between 0 and 1. On the bottom right corner, you can see that it took <code>0:00:00.000654</code>
+      or 0 hours, 0 minutes, 0 seconds and only 654ns, or 0.6ms. This run time is
+      good to keep an eye on. It will help build an intuition for how fast Postgres
+      really is, and how certain operations scale as the data grows. You''ll be able
+      to see how long </p>
 
       <p>Try rerunning the cell again by clicking the "play" button in the top right
       corner. You''ll see that the random number will change. Rerunning is a real
@@ -237,14 +227,19 @@
 
 
       Thank you for trying out PostgresML! We hope you enjoy your time here and have
+      fun learning about machine learning, in the comfort of your favorite database.
+
 
-      fun learning about machine learning, in the comfort of your favorite database.'
+      You may want to check out the [rest of the tutorial''s](/nb/) or dive straight
+      in with a notebook to test [real time fraud detection](/notebooks/notebook/1/).'
     rendering: '<article class="markdown-body"><h3>Thank you</h3>
 
       <p>Thank you for trying out PostgresML! We hope you enjoy your time here and
-      have
+      have fun learning about machine learning, in the comfort of your favorite database.</p>
 
-      fun learning about machine learning, in the comfort of your favorite database.</p></article>'
+      <p>You may want to check out the <a href="/nb/">rest of the tutorial''s</a>
+      or dive straight in with a notebook to test <a href="/notebooks/notebook/1/">real
+      time fraud detection</a>.</p></article>'
     execution_time: null
     cell_number: 4
     version: 1
@@ -255,11 +250,8 @@
     notebook: 0
     cell_type: 3
     contents: SELECT 'Have a nice day!' AS greeting;
-    rendering: "<div class=\"markdown-body\">\n<table>\n  <thead>\n    <tr>\n      \n
-      \     <td><strong>greeting</strong></td>\n      \n    </tr>\n  </thead>\n  <tbody>\n
-      \   \n    <tr>\n      \n      <td>Have a nice day!</td>\n      \n    </tr>\n
-      \   \n  </tbody>\n</table>\n</div>\n"
-    execution_time: '00:00:00.000580'
+    rendering: null
+    execution_time: null
     cell_number: 5
     version: 1
     deleted_at: null
