WSL Local Workshop V1.2

In this workshop you will learn how to develop and deploy applications in WSL Local. The workshop has been divided into several stand-alone parts for those who are interested in a specific development tool or deployment task.

This lab is meant to be instructor-led. That is, the instructor will explain the objectives of the WSL capabilities covered in each lab, and demonstrate some of those capabilities at the beginning of each lab.

About this repository

This repository contains several lab subfolders. Some labs include notebooks and data, while others have additional instructions that are located in the Lab Instructions folder.

Prerequisites

1. Knowledge of analytics. These labs do not teach you the basics of analytics or how to implement analytics in R, Python and SPSS. The purpose of this workshop is to provide hands-on experience with analytics tools and deployment functions in WSL Local.

2. To run this workshop you need an instance of WSL Local V1.2.3 or above.

3. Download WSL_Demos.zip.

Setting up lab projects in WSL Local

1. Rename the downloaded WSL_Demos.zip file and give it a unique name. For example, add your initials (i.e. WSL_Demos_JP.zip). Note: Project names in WSL Local cluster must be unique. When we create a project "from file", the project name is inherited from the file name.
2. Login to WSL Local.
3. Select "New Project" and select "From File".
4. Browse to the .zip file and click Create. .

Lab 1: Build, Save and Test SparkML Models (Jupyter/Python)

1. Open the project you just created.
2. Navigate to Assets tab, select Notebooks and open TelcoChurn_SparkML_35a Jupyter notebook. This notebook has been implemented for the Python 3.5 runtime. You can verify the runtime by running the first cell in the notebook.
3. Follow instructions in the notebook.

Review:

1. Save and checkpoint
2. How to run a cell
3. Restarting the kernel (pixiedust)
4. Testing the model
5. Setting the scoring endpoint
6. Stopping the kernel

Goals:

1. Familiarity with Jupyter notebook -- note time it takes to start notebook
2. Importing libraries (included in image and external i.e. pixiedust)
3. Save model to repository (dsx_ml package)
4. Test model via UI -- also view installed packages
5. Test model via REST
6. Note notebook is still running

Lab 2: Build, Save and Test Scikit-Learn Models (Jupyter/Python)

1. Navigate to Assets view and open CreditCardDefault_SkLearn notebook.
2. Follow instructions in the notebook.

Review:

1. Jupyter magics (i.e. %brunel, %matplotlib)

Goals:

1. Note faster notebook start time
2. %brunel cells

Lab 3: Build, Save and Test SparkML models (Zeppelin/Python)

1. Navigate to Assets view and open TelcoChurn_Zeppelin notebook.
2. Follow instructions in the notebook. Be sure to put the Spark interpreter at the top of the interpreter list

Review:

1. Interpreter bindings
2. Running paragraphs
3. Zeppelin magics
4. Graph interactions

Goals:

1. Note multiple interpreters
2. Multiple magics i.e. %sql, %spark.pyspark

Lab 4: Build R models in Jupyter and deploy into Shiny App

1. Follow the instructions in Lab Instructions/R_in_WSL.pdf

Review:

1. Jupyter notebook but R
2. system() calls -- underlying filesystem
3. Running RStudio
4. Publishing to Shiny server (unique name!)
5. Content visibility
6. Permalink (please save this when shown)

Goals:

1. R notebook
2. Loading R libraries
3. Saving model to RStudio File System (this will NOT show in Models view)
4. RStudio / Shiny
5. Publishing to Shiny

Lab 5: Watson Machine Learning

1. Follow the instructions in Lab Instructions/WatsonMachineLearning.pdf

Goals:

1. Uploading Data Sets
2. Introduction to Watson Machine Learning

Lab 6: SPSS Modeler in WSL

1. Follow the instructions in Lab Instructions/SPSS_Modeler_in_WSL.pdf

Goals:

1. Introduction to SPSS Modeler

Lab 7: Create Batch Script and Test Batch Scoring

1. You must have completed "Lab 1: Build, Save and Test SparkML Models" before working through this lab.
2. Navigate the to the Models section of the project and click into the saved Telco_Churn_ML_model.
3. Click the Batch score tab.
4. For Input data set, select TelcoModelFeedback.csv.
5. For Output data set, check "Local file" and specify new_customers_scores.csv.
6. On the top right, click Advanced Settings.
7. Scroll through the Advanced Setting to see the various options. Click Save to save the default settings.
8. Click Generate Batch Script. (Note: the batch script can be edited. For example, to perform pre/post processing tasks)

9. Click Run now and wait till the status changes to Success.
10. Verify that the new_customers_scores.csv is in the data section of the project.

Review:

1. Batch scoring
2. Options (parameters, scheduling)

Goals:

1. Introduction to batch scoring

Lab 8: Create Model Evaluation Script and Test Evaluation

1. You must have completed "Lab 1: Build, Save and Test SparkML Models" before working through this lab.

2. Navigate the to the Models section of the project and click into the saved Telco_Churn_ML_model.

3. Click the Evaluate tab.

4. For the scripts inputs, specify these values.
   

5. Click Advanced Settings and change the name of the script. For example, you can name it ChurnModelEvalScript. Click Save.

6. Click Generate evaluation Script.

7. Click Run now and wait till the status changes to Success.

8. Scroll down to review the results.
   

Lab 9: Deploy Project into Production -- review WSL_Deployment.pdf in Lab Instructions

1. Follow the instructions in Lab Instructions/WSL_deployment.pdf

Lab 10: WSL Data Sources

1. Bring up DatabaseExample notebook.
2. DB user is dash100580
3. DB password is W2bOD_Cg2db_

Lab 11: Decision Optimization

1. Bring up "Use DO to schedule sports games Local" notebook.
2. Run Decision Optimization model Wizard
3. https://content-dsxlocal.mybluemix.net/docs/content/SSAS34_current/DODS/DODS_Mdl_Assist/exhousebuild.html