Make ML notebook work with Dev Edition and IBM Cloud

* Make the ML deployment work with IBM Cloud ML for Dev Edition
* Add final arch/flow diagram
* Add usual files
* Add ACK

ACKNOWLEDGEMENTS.md

@@ -0,0 +1,4 @@
+# Acknowledgements
+
+* Credit goes to [Frank Sun](https://twitter.com/sun_xi_frank) for the original notebooks and documentation that became this code pattern.
+* The content was taken from https://github.com/IBMProjectEventStore/db2eventstore-IoT-Analytics and adapted to be more Developer Edition friendly.

CONTRIBUTING.md

@@ -0,0 +1,16 @@
+# Contributing
+
+This is an open source project, and we appreciate your help!
+
+We use the GitHub issue tracker to discuss new features and non-trivial bugs.
+
+In addition to the issue tracker, [#journeys on
+Slack](https://dwopen.slack.com) is the best way to get into contact with the
+project's maintainers.
+
+To contribute code, documentation, or tests, please submit a pull request to
+the GitHub repository. Generally, we expect two maintainers to review your pull
+request before it is approved for merging. For more details, see the
+[MAINTAINERS](MAINTAINERS.md) page.
+
+Contributions are subject to the [Developer Certificate of Origin, Version 1.1](https://developercertificate.org/) and the [Apache License, Version 2](https://www.apache.org/licenses/LICENSE-2.0.txt).

MAINTAINERS.md

@@ -0,0 +1,69 @@
+# Maintainers Guide
+
+This guide is intended for maintainers - anybody with commit access to one or
+more Code Pattern repositories.
+
+## Methodology
+
+This repository does not have a traditional release management cycle, but
+should instead be maintained as a useful, working, and polished reference at
+all times. While all work can therefore be focused on the master branch, the
+quality of this branch should never be compromised.
+
+The remainder of this document details how to merge pull requests to the
+repositories.
+
+## Merge approval
+
+The project maintainers use LGTM (Looks Good To Me) in comments on the pull
+request to indicate acceptance prior to merging. A change requires LGTMs from
+two project maintainers. If the code is written by a maintainer, the change
+only requires one additional LGTM.
+
+## Reviewing Pull Requests
+
+We recommend reviewing pull requests directly within GitHub. This allows a
+public commentary on changes, providing transparency for all users. When
+providing feedback be civil, courteous, and kind. Disagreement is fine, so long
+as the discourse is carried out politely. If we see a record of uncivil or
+abusive comments, we will revoke your commit privileges and invite you to leave
+the project.
+
+During your review, consider the following points:
+
+### Does the change have positive impact?
+
+Some proposed changes may not represent a positive impact to the project. Ask
+whether or not the change will make understanding the code easier, or if it
+could simply be a personal preference on the part of the author (see
+[bikeshedding](https://en.wiktionary.org/wiki/bikeshedding)).
+
+Pull requests that do not have a clear positive impact should be closed without
+merging.
+
+### Do the changes make sense?
+
+If you do not understand what the changes are or what they accomplish, ask the
+author for clarification. Ask the author to add comments and/or clarify test
+case names to make the intentions clear.
+
+At times, such clarification will reveal that the author may not be using the
+code correctly, or is unaware of features that accommodate their needs. If you
+feel this is the case, work up a code sample that would address the pull
+request for them, and feel free to close the pull request once they confirm.
+
+### Does the change introduce a new feature?
+
+For any given pull request, ask yourself "is this a new feature?" If so, does
+the pull request (or associated issue) contain narrative indicating the need
+for the feature? If not, ask them to provide that information.
+
+Are new unit tests in place that test all new behaviors introduced? If not, do
+not merge the feature until they are! Is documentation in place for the new
+feature? (See the documentation guidelines). If not do not merge the feature
+until it is! Is the feature necessary for general use cases? Try and keep the
+scope of any given component narrow. If a proposed feature does not fit that
+scope, recommend to the user that they maintain the feature on their own, and
+close the request. You may also recommend that they see if the feature gains
+traction among other users, and suggest they re-submit when they can show such
+support.

README.md

@@ -35,15 +35,6 @@ After completing this code pattern, you’ll understand how to:
 6. [Analyze the data](#6-analyze-the-data)
 7. [Create and deploy a machine learning model](#7-create-and-deploy-a-machine-learning-model)
 
-<!--
-1. [Visualize the data](#6-visualize-the-data)
-2. [Create Watson services with IBM Cloud](#2-create-watson-services-with-ibm-cloud)
-2. Setup a Event Store database and table
-3. Perform multiple data science tasks with Event Store using Jupyter notebooks
-4. Interact with Event Store database using REST API
-5. Run example IBM Streams and Remote Access Applications with Event Store
--->
-
 ### 1. Clone the repo
 
 Clone the `db2-event-store-iot-analytics` repo locally. In a terminal, run:
@@ -132,14 +123,29 @@ If you are using the **Developer Edition** of Db2 Event Store, you need an IBM C
 
 * Sign in and create the service [here](https://console.ng.bluemix.net/catalog/services/machine-learning).
 * Click on `Service credentials` and then `New credential` and `Add`.
-Use `View credentials` and copy the credentials JSON.
+* Use `View credentials` and copy the credentials JSON.
 * You will use the JSON to set the `wml_credentials` variable in the notebook.
+* The notebook will pip install watson-machine-learning-client. After the install, you usually need to restart your kernel and run the notebook again from the top.
 
 Load the notebook, using the file **`Event_Store_Data_Analytics.ipynb`**.
 
+Once the model is built and deployed, you can easily send it a new measurement and get a predicted temperature (one at a time or in batches).
+
+##### Given a new data point
+
+```python
+new_data = {"deviceID" : 2, "sensorID": 24, "ts": 1541430459386, "ambient_temp": 30, "power": 10}
+```
+
+##### The result includes a predicted temperature
+
+```python
+predictions:  [48.98055760884435]
+```
+
 ## Sample output
 
-See the notebooks with example output here: [notebook examples with result](examples)
+See all the notebooks with example output [here](examples).
 
 ## License
 

examples/Event_Store_Data_Analytics.ipynb

@@ -1932,7 +1932,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "deletable": true,
     "editable": true
    },
@@ -1978,7 +1977,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,