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Domain-driven Design for Machine Learning products

Training 2 days

Dates and Booking

-- Description

Want to develop meaningful, innovative, and data-driven software products? Don‹t start by evaluating machine learning algorithms. The first step is to find and verify an AI/ML use case so that AI/ML helps you solve a relevant problem. However, the overall process from use case discovery to deployment and operation of ML models is not a trivial undertaking.

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

Day 1: In this interactive training, we convey the possibilities of ML/AI and show which problems are predestined for it. Then we briefly introduce you to the basic ideas, concepts, and patterns of Domain-driven Design.

In the first practical part we get to know Event Storming and the ML Design Canvas. Event Storming is a method from Collaborative Modeling and Knowledge Crunching. It helps domain experts, technical experts, developers and all other team members to develop a common understanding of the business domain. This then allows us to identify use cases for innovative AI/ML technologies.

Day 2: We collaboratively formulate ML problems based on the use cases we have found. We do this on the ML Design Canvas. Then we structure the ML project on the Canvas and specify all components for the training and prediction phases. Afterwards, we talk about the Data Landscape Canvas to clarify the data availability.

-- 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 and structure them using the ML Design Canvas

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

-- Audience

Software architects, developers, data scientists, product owners

-- Training Objectives

Find out which problems and use cases are suitable for ML

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

Define and prioritize problems and opportunities for ML in business areas

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

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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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