W7067G - Watson Studio Methodology

1. Data science and AI

• Describe the value of artificial intelligence
• Explain the AI ladder approach and AI lifecycle
• Identify the roles for working with data and AI

2. Watson Studio

• Summarize the benefits of Watson Studio
• Outline the integration of Watson Studio and Watson Machine

3. Learning

• List and explain the tools available in Watson Studio
• Sign up for a free IBM Watson account

4. Watson Machine Learning

• Describe machine learning methods and how they fit with AI
• Create a Watson Studio project for learning models

5. Watson Knowledge Catalog

• Explain the features of Watson Knowledge Catalog
• Identify the role of data policies to govern data assets
• List and describe the data files used in this course
• Create a catalog, add assets to a catalog, and add catalog assets to a project

6. Data refinement

• List the steps to successful data mining
• Describe the typical customer churn business problem
• Identify the steps in the data refinement process
• Shape a data set using the Data Refinery according to specific observations

7. Data modeling

• Differentiate the Watson Studio tools to create models
• Create a Watson Machine Learning model using AutoAI
• Create a Machine Learning model using SPSS Modeler
• Build a model using SparkML Modeler Flow

8. Data science with notebooks

• Experiment with Jupyter notebooks
• Load from a file and run a Jupyter notebook with Watson Studio

9. Model deployment

• Identify the model repository
• List model deployment and test options
• Deploy a model
• Test a deployed model