Real-Time Decisions Introduction Describing the business purpose and features of RTD Describing Rules and Models in the Decision Framework Describing the capabilities of RTD Applications RTD Architecture Identifying architecture components of the RTD platform and describing their roles Exploring aspects of the installation and configuration of the RTD platform Describing integration support Exploring Decision Studio and Inline Services Describing an inline service and explaining its role in RTD Describing Decision Studio and explaining its role in configuring and deploying inline services Identifying and describing the components of the Decision Studio user interface Identifying and describing the key elements in an inline service Exploring Load Generator Describing the purpose of the Load Generator utility Simulating the run-time operation of an inline service using Load Generator Using Load Generator for testing Using Load Generator performance characterization Exploring Decision Center
Describing the purpose and capabilities of Decision Center Navigating the Decision Center user interface Describing Decision Center reports Modifying and redeploying an inline service using Decision Center Creating a Basic Inline Service Building a basic inline service Creating and configuring Application, Data Source, Entity, and Informant inline service elements Deploying and testing an inline service Creating a Model for Call Analysis Adding informants to an inline service Creating choice groups and choices Creating a model to analyze call reasons Populating a model using the Load Generator utility Analyzing results in Decision Center Using the JConsole administration tool to reset model learnings Generating Offers Based on Performance Goals Creating performance goals and using them to score offers Generating cross-sell offer recommendations using an advisor Configuring the Inline Service to Learn on Offer Acceptances Configuring the inline service to learn on offer acceptances Tracking the success of offers by using events in the lifetime of an offer Using a Model to Influence Offer Generation Configuring the inline service to predict the likelihood of offer acceptance Influencing inline service models to present offers based on learnings Adding artificial bias for a particular offer RTD Predictive Analytics Describing the concept of predictive analytics Describing the RTD decision process Defining the benefits of real-time modeling and scoring over traditional data mining Combining rule-driven and model-driven logic Interpreting RTD real-time model reports and insights Understanding concepts of model quality and maturation Composite Decisions Understanding the use of external objects in inline services Describing dynamic choices and comparing them with static choices Creating dynamic choices in Decision Studio Describing external rules and external goals Setting up external rules RTD Administration Migrating inline services from development to production Describing the purpose and use of Java Management Extensions (JMX) Management Console Administering RTD and inline services using JMX Management Console Real-Time Decisions Batch Framework
Describing the RTD batch framework and its architecture and components Implementing the batch job interface and registering batch jobs Running and monitoring batch jobs RTD Integration Describing how RTD integrates with target applications Describing RTD integration support options Understanding options: Java Smart Client, Web Service Client, and others
Description:
This course, designed for individuals on the implementation team responsible for inline service development and RTD installation and administration, enables participants to perform tasks required to successfully configure and deploy RTD with their operational applications and leverage its provision of decisions as a service. Participants learn about inline services and the elements that support real-time decisions, including the use of business and filtering rules as well as the role of automated RTD learning and adjustment based on unique transactional interactions. The course covers various aspects of integration between RTD and target applications as well as administrative tasks and tools and the use of the RTD batch framework, preparing participants to engage on RTD deployment projects at all levels of the project lifecycle, from gathering requirements to production rollout and monitoring.
The course introduces the RTD platform and applications, describing their features, functions, capabilities, and architecture. The lesson topics are reinforced with structured hands-on practices during which participants create and deploy a fully functional inline service project from scratch. Participants also perform administrative tasks and use the batch framework to obtain batched decisions, simulate responses, and close the loop with batched learning.
Simulate the runtime operation of the inline service project
Evaluate integration options between RTD and target operational applications
Interpret RTD learning and statistics using real-time model reports
Use dynamic choices and external rules to manage composite decisions
Implement and perform batch operations using the batch framework
Build an inline service project and manage its deployment
