In these workshop lab exercises, we use IBM Cloud Pak for Data to go through the whole data science pipeline to solve a business problem with the help of 2 use case - Customer Churn for a Telecom company and Fraud prediction in Auto Insurance claims. Cloud Pak for Data is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (e.g., RStudio, Jupyter Notebooks, Spark, etc.) to collaborate, share, and gather insight from their data as well as build and deploy machine learning and deep learning models.
When the reader has attended and completed lab tutorials in the workshop, they will understand how to:
- Create a project in Cloud Pak for data
- Use Jupyter Notebooks to load, visualize, and analyze data
- Run Notebooks in IBM Cloud Pak for Data
- Build, test, and deploy a machine learning model using Scikit-learn on IBM Cloud Pak for Data.
- Deploy a selected machine learning model to production using Cloud Pak for Data
- Test the deployed model using the exposed endpoint
- Setup Watson OpenScale Data Mart
- Bind Watson Machine Learning to the Watson OpenScale Data Mart
- Add subscriptions to the Data Mart
- Enable payload logging and performance monitor for subscribed assets
- Enable Quality (Accuracy) monitor
- Enable Fairness monitor
- Score the prediction models using the Watson Machine Learning
- Jupyter Notebooks: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text.
- Pandas: An open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
- Scikit-learn: An open source machine learning library in Python