direkshan-digital / dsx_local_workshop

DSX Local Workshop

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

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 DSX.
2. To run this workshop you need an instance of DSX Local. Please note that while most code is the same between DSX Local and DSX Cloud, the notebooks included in sample projects will work in DSX Local only
3. Download and unzip this this repository. Unzip the repository only, not files in subfolders.

Setting up lab projects in DSX Local

1. Rename DSX_Local_Workshop.zip located in DSX_Local_Projects folder of the unzipped repository to a unique name, for example, add your initials. Note: Project names in DSX Local cluster must be unique. When we create a project "from file", the project name is inherited from the file name.
2. Log in to DSX Local.
3. Select "Create New Project" and select "From File".
4. Browse to the .zip file and click Create. .

Use Case 1: Improve customer retention (SparkML models in Jupyter/Python)

1. Open the project you just created.
2. Navigate to Assets view and open TelcoChurn_SparkML Jupyter notebook. This notebook has been implemented for the Python 2.7 runtime. You can verify the runtime by running the first cell in the notebook.
3. Follow instructions in the notebook.
4. If you would like to see an example of invoking a published model with a REST client, import the TelcoChurn_Invoke_Deployed_Model.jupyter.ipynb notebook into your project and follow instructions in the notebook.

Use Case 2: Improve operational efficiency (Scikit-learn and SparkML models in Jupyter/Python)

1. Open the project you just created.
2. Navigate to Assets view and open CreditCardDefault_SkLearn notebook. If you want to stay with the telco churn example, you can work through the TelcoChurn_SkLearn notebook.
3. Follow instructions in the notebook.

Use Case 3: Improve customer retention (SparkML models in Zeppelin/Python)

1. Open the project you just created.
2. Navigate to Assets view and open TelcoChurn_Zeppelin notebook.
3. Follow instructions in the notebook.

Use Case 4: Data science for the automotive industry (R models in Jupyter and Shiny)

1. Follow instructions in the R_in_DSX.pdf in the Lab Instructions folder of the unzipped repository.

Use Case 5: Improve customer retention and fraud prevention (SPSS Modeler in DSX)

1. Follow instructions in the SPSS_Modeler_in_DSX.pdf document in the Lab Instructions folder of the unzipped repository.

Use Case 6: Batch deployment of analytics (Batch Scoring in DSX)

1. Follow instructions in the DSX_Batch_Scoring.pdf document in the Lab Instructions folder of the unzipped repository.

Use Case 7: DSX - a deployment platform for different types of models (PMML in DSX)

1. Follow instructions in the DSX_PMML_Lab.pdf document in the Lab Instructions folder of the unzipped repository.

Use Case 8: DSX - a platform that supports analytics application lifecycle (Model Evaluation in DSX)

1. Follow instructions in the DSX_Evaluation_in_DSX.pdf document in the Lab Instructions folder of the unzipped repository.

Use Case 9: Data access in DSX (databases and Hadoop)

1. Follow instructions in the DSX_Data_Access.pdf document in the Lab Instructions folder of the unzipped repository.

Use Case 10: Deep learning in DSX

1. Create a project from DSX_Deep_Learning.zip located in the DSX_Local_Projects folder (make sure to rename the project before you create a project from file).
2. Work through sample notebooks.

Use Case 11: Remote Spark Execution

1. Follow instructions in the Remote Spark Execution.pdf document in the Lab Instructions folder of the unzipped repository.