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About the Operationalizing Machine Learning With DMN Training Course
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.
You Will Learn How To
- 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
This three-part online training class will help you be immediately effective in operationalizing your machine learning models. You’ll learn how to use decision modeling to frame machine learning projects and evaluate the effectiveness of your machine learning models in business terms. You’ll understand the different ways machine learning can be used to improve decision-making and how to use of business rules technology alongside machine learning. You’ll see how decision modeling complements CRISP-DM and how the combination makes your machine learning projects more effective. Interactive work sessions support each step, focused on problems that reinforce key points.
- Overview of CRISP-DM and its basic approach
- The importance of decisions in the Business Understanding phase
- Decision modeling as a way to assess the situation and set goals for the project
- Decision-centric approach to Data Understanding phase of CRISP-DM
- Decision-centric approach to Data Preparation and Modeling phase of CRISP-DM
- Decision-centric approach to Evaluation phase of CRISP-DM
- Decision-centric approach to Deployment phase of CRISP-DM
- Technical deployment options
- Business rules in a decision model to turn predictive analytic into prescriptive one
- Ongoing decision (not just model) monitoring and management
We encourage you sign up now and invite your colleagues. This training is the first step toward transforming your decision-making and achieving better business outcomes.
Decision Modeling with DMN Training
Want to learn more about decision modeling? Register to learn how to use decision models in a Live training led by experienced decision modelers. There’s a great discount and you can sign up for both classes in one go on the registration form.
About the Instructor
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.
More Details About the Operationalizing Machine Learning With DMN Training Course:
Who Should Attend
- Decision makers
- Professionals interested in a broad overview and introduction
General Information for Attendees
- Format – This is a live training. Recordings will be sent out after each session in case you miss the session or want to want to review at a later time. For those who live in time zones where the session is scheduled outside working hours, a separate work session to go over homework can be scheduled.
- Recommended Software – It is recommended that participants use a decision modeling software for the exercises, such as DecisionsFirst Modeler. However, use of this software is optional, and other diagramming software, modeling tools, or paper and pencil can also be used. The techniques taught are equally applicable. Course examples will be in a class installation of DecisionsFirst Modeler. All participants will receive a free trial of the recommended software.
- Cancellation Policy – Registrations may be canceled up to 48 hours in advance of the first class. A 15% administrative fee will be retained if you choose to cancel. To cancel, please contact us.
- Technology Check – The class will be conducted in Zoom. You can test your connection, video and microphone at https://zoom.us/test. Although we will use chat and Q&A to communicate in the class, it is also helpful if we are able to have audio interactions. For audio interaction you need a microphone or headset connected to your computer. You can test that it is working using the Zoom test link above.
- Other Terms – If for any reason the class cannot take place as scheduled, you will be offered alternative training dates at the same price.
What Others Have Said About the Course
“Instead of beginning with the data, I now know to start with what decision I am trying to make.”
“The course really helped me understand how decision modeling ties together business process, business rules and predictive analytics.”
“Even simple decision requirements diagrams quickly bring clarity and focus to analytic problems.”
“Decision modeling would have been invaluable in many past projects, saving significant time.”
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