Overview

This course is designed to teach data scientists, Operational Research specialists, and business analysts how to build, run and deploy basic optimization models using IBM Decision Optimization for IBM Cloud Pak for Data. You will learn how to create projects to manage your resources, use the user-friendly Model Builder interface to build, run and visualize models, use different methods to formulate models, including using the Modeling Assistant for formulate your model in natural language. You will also learn how to import and use python notebooks to create your models, and how to deploy the solution to end users within Cloud Pak for Data.

Audience

Data scientists, Operational Research specialists, or business analysts who are new to Decision Optimization for IBM Cloud Pak for Data.

Prerequisites

• Knowledge of your business requirements
• If you are planning to create or edit a model using Python or OPL, you require the skills and knowledge needed to convert a business problem to a mathematical model

Objective

• Introduction to IBM Decision Optimization for IBM Cloud Pak for Data • Create models using Model Builder  • Create models using Python  • Deploy a model

Course Outline

Introduction to IBM Decision Optimization for IBM Cloud Pak for Data • Describe what Decision Optimization is • Describe the five stages in a Decision Optimization project • Install Decision Optimization on IBM Cloud Pak for Data • Create projects to manage resources • Access the Decision Optimization service • Add data to your project Create models using Model Builder  • Prepare data • Run the model • Explore the solution • Run a new scenario  Create models using Python  • Use the DOcplex library to create models using Python  • Formulate your model using Create or Import  • Work with Jupyter notebooks • Learn about python coding from sample notebooks Deploy a model • Explain how to review a model • Explain how to deploy a model