Workshop – Machine Learning Operationalized for Business: Ensuring ML Deployment Delivers Value

Workshop Description

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it. To get to that point, your business must follow a gold standard project management process, one that is holistic across organizational functions and reaches well beyond executing the core number crunching itself.

At this workshop, you will gain a deep understanding of the concepts and methods involved in operationalizing machine learning to deliver business outcomes. This workshop focuses on the elements of a machine learning project that define and scope the business problem, ensure that the result is useful in business terms, and help deliver and operationalize the machine learning outcome. Based on CRISP-DM – the most well known, established industry standard management process for machine learning – this course does not dive into the core machine learning technology itself, but focuses instead on how machine learning must be applied in order to be effective. Attendees will have opportunity to apply what they learn to real-life scenarios.

Key Topics:

• Apply machine learning to business operations through the structure of CRISP-DM
• Use decision modeling to understand real-world business problems in a way that allows machine learning to be applied effectively
• Take a decision-centric and business-focused approach to machine learning projects
• Evaluate and deploy machine learning results to minimize the gap between analytic insight and business improvement

Coverage of the CRISP-DM Project Management Phases:

• An overview of CRISP-DM and its basic approach
• Discuss and demonstrate the importance of decisions in the Business Understanding phase
• Introduce and teach decision modeling as a way to assess the situation and set goals for the project
• Discuss decision-centric approach to Data Understanding phase of CRISP-DM
• Discuss decision-centric approach to Data Preparation and Modeling phase of CRISP-DM
• Discuss decision-centric approach to Evaluation phase of CRISP-DM
• Discuss decision-centric approach to Deployment phase of CRISP-DM
• Brief discussion of technical deployment options
• Specification of business rules in a decision model to turn predictive analytic into prescriptive one
• Importance of ongoing decision (not just model) monitoring and management

Learning Objectives:

• Frame data quality and other data needs in decision-centric terms
• Evaluate machine learning outputs against decision models to determine business value
• Use decision models to show how machine learning results can be captured and compared
• Understand different ways in which machine learning can be used to improve decision-making
• Read and understand a decision model built using the Decision Model and Notation (DMN) standard
• Develop basic decision modeling skills for use on machine learning projects
• Understand how decision modeling complements CRISP-DM as an approach to machine learning
• Understand technology architecture required for machine learning project deployment
• Be able to use decision model to frame organizational and process change requirements for machine learning project
• Understand use of business rules and business rules technology alongside machine learning

Instructor

James Taylor, CEO, Decision Management Solutions

James Taylor is the CEO of Decision Management Solutions and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.