Intended Audience: Managers, decision makers, practitioners, and professionals interested in successfully deploying machine learning

Knowledge Level: All levels

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 approach, 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 bizML – the gold-standard industry framework for running machine learning projects – this course does not dive into the core machine learning technology itself, but focuses instead on the business practice needed to get ML deployed. Attendees will have opportunities to apply what they learn to real-life scenarios.

Attendees will receive copies of both “Digital Decisioning: Using Decision Management to Deliver Business Impact from AI” by James Taylor and “The AI Playbook: Mastering the Rare Art of Machine Learning Deployment” by Eric Siegel.

Developed by Machine Learning Week founder Eric Siegel, bizML is an updated, business-focused framework designed to succeed the prior incumbent framework, CRISP-DM. It is presented by Siegel’s book, The AI Playbook, and summarized in this Harvard Business Review article.

Key Workshop Topics:

• Apply machine learning to business operations following the bizML paradigm
• 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
• The cutting-edge techniques and technologies needed to successfully implement the bizML practice
• Leverage generative AI to execute on decision modeling and bizML

BizML and Decision Modeling

• An overview of bizML and its basic approach
• The bizML six-step playbook:
  1. Value: Establish the Deployment Goal
  2. Target: Establish the Prediction Goal
  3. Performance: Establish the Evaluation Metrics
  4. Fuel: Prepare the Data
  5. Algorithm: Train the Model
  6. Launch: Deploy the Model
• Plus
  1. The importance of decisions in establishing value
  2. Decision modeling as a way to assess the situation and set goals for the project
  3. How decision thinking helps in preparing the data and training the model.
  4. The role of decision modeling in successful deployment and operationalization.
  5. A brief discussion of technical deployment options and complementary technology
  6. How to maximize the value of GenAI in ML projects.
  7. After step 6: upkeep –the importance of ongoing model and decision 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 bizML 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 the use of business rules and business rules technology alongside machine learning