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Domain-driven Design for Machine Learning products

Training 2 days

Dates and Booking


You are faced with the task of developing innovative, data-driven software products and have already gained some practical experience with machine learning. Now the question arises: Which problem area(s) can be addressed in product development with ML?

Sounds familiar? Then this workshop is just right for you! Learn from our trainers how to find and verify AI/ML use cases and get the right tools and procedures for product finalization. We use effective DDD methodology (no previous DDD knowledge necessary).

Dr. Larysa Visengeriyeva during a workshop break in an exchange with participants

Day 1: In the first practical part, we will learn about Event Storming and the ML Design Canvas. Event Storming is a method of Collaborative Modeling and Knowledge Crunching, a methodology of Domain-Driven Design. It helps domain experts, technical experts, developers and all other team members to develop a common understanding of the business domain. In doing so, we start from use cases examples, and so we can then identify further use cases in a project for innovative AI/ML technologies. Prior Domain-Driven Design knowledge is not necessary.

**Day 2: In the second practical part, we formulate concrete ML problems together, based on the use cases we have found. We do this on the ML Design Canvas, which was introduced on the first day. Afterwards, we structure the ML project on the canvas and specify all components for the training and prediction phases. Then we discuss the Data Landscape Canvas to clarify the data availability as last step.

Your Benefits

Overview of the main concepts of Domain-driven Design

Understand how to analyze a domain with Event Storming

Understand how to find AI/ML use cases in your projects, and structure them using the ML Design Canvas

Be able to conduct your own AI/ML Event Storming workshops


Software architects, developers, data scientists, product owners, who have made their first practical experiences with Machine Learning.

Training Objectives

Find out which problems and use cases are suitable for ML

Learn the knowledge crunching method of event storming used in DDD in a case study and apply it yourself

Define and prioritize problems and opportunities for ML in business areas and projects

Learn how to use the Machine Learning Canvas to structure ML projects

Learn how to use the Data Landscape Canvas

Your Trainers

Dr. Larysa Visengeriyeva

Machine Learning and MLOps

Christopher Stolle

Sustainable software architecture, DDD and meaningful use of technology to solve business problems

Isabel Bär

Sustainable and responsible use of machine learning

Technical Information and Books

Fairness and Artificial Intelligence

Classical software testing cannot simply be transferred to AI. Model governance and internal audits are required to ensure fairness. Read more

The comprehensive resource on everything MLOps. Created and maintained by Larysa Vinsegeriyeva. Read more

Online Courses

On-site Courses

Dates by Request

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In-House Training

You can also book this training as an in-house training course exclusively for your team. Please use the enquiry form for more details.

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