Cloud Pak for Data provides you with the environment and tools to solve your business problems by collaboratively working with data. You can choose the tools you need to analyze and visualize data, to cleanse and shape data, to ingest streaming data, or to create, train, and deploy machine learning/deep learning models. Cloud Pak for Data contains both open source and IBM value-add capabilities to help infuse AI into business to drive innovation.
Cloud Pak for Data includes Watson Knowledge Catalog. Watson Knowledge Catalog is a secure enterprise catalog to discover, catalog and govern your data/models with greater efficiency. The catalog is underpinned by a central repository of metadata describing all the information managed by the platform. Users will be able to share data with their colleagues more easily, regardless of what the data is, where it is stored, or how they intend to use it. In this way, the intelligent asset catalog will unlock the value held within that data across user groups—helping organizations use this key asset to its full potential.
The labs in this workshop will illustrate the myriad features included in Cloud Pak for Data, including Watson Studio, Watson Knowledge Catalog, Watson Machine Learning and Watson OpenScale. The datasets used for the lab contain simulated data.
-
Lab-1 - This lab will set up the Cloud Pak for Data environment for subsequent labs and introduce you to the Project and Gallery features.
-
Lab-2 - This lab will introduce you to the features of IBM's Watson Knowledge Catalog. Watson Knowledge Catalog is a secure enterprise catalog to discover, catalog and govern your data and modeling assets with greater efficiency.
-
Lab-3 - This lab will introduce the Data Refinery. Data Refinery is a self-service data preparation tool for data scientists, data engineers, and business analysts. Data Refinery provides profiling, visualization, and a robust set of transforms to prepare data for analytics purposes. We will continue to use the 3 Trafficking data sets in this lab to demonstrate data profiling, data visualization, and data preparation capabilities of the Data Refinery tool. Note the datasets use simulated data.
-
Lab-4 - In this lab, you will use the Watson SPSS Modeler capability to explore, prepare, and model the trafficking data. The SPSS Modeler is a drag and drop capability to build machine learning pipelines.
-
Lab-5 - In this lab, you will use SparkML in Watson Studio to run simulated travel data through a machine learning algorithm, automatically tune the algorithm, and load the data into a DB2 Warehouse database. If you did not successfully complete Lab-2, please go to Lab-9 to do the notebook lab.
-
Lab-6 -This lab consists of two parts. The first part will demonstrate the new and exciting AutoAI capability to build and deploy an optimized model based on the trafficking data sets. The second part will deploy an application using the IBM Cloud DevOps toolchain that will invoke the deployed model to predict the human trafficking risk.
-
Lab-7 - This lab will feature Watson OpenScale. IBM Watson OpenScale is an open platform that helps remove barriers to enterprise-scale AI.
-
Lab-8 - This lab will feature the Decision Optimization Modeling Assistant to define, formulate, and run a decision optimization applied to house construction.
-
Lab-9 - This lab will fulfill the prerequisites for Labs 3,4, and 5, if Lab-2 is not completed successfully.
-
Lab-9a - This lab is a replacement for Lab-5 if Lab-2 is not completed successfully