DDD for Machine Learning / AI use cases
An important reason why companies fail to implement artificial intelligence and / or machine learning is the difficulty of identifying a meaningful use cases for machine learning with a shared understanding between domain experts, ML specialists, data scientists and developers.
In this hands-on workshop we will demonstrate how we can use ideas from Domain-Driven Design, Collaborative Modelling and Canvasses to develop a common understanding of our product, identify AI/ML Use Cases for innovation and structure a machine learning project
If you want to develop good, innovative and data-driven software products, you should not start by evaluating machine learning algorithms. The first step should be to find and verify an AI/ML Use Case so that the use of AI/ML will solve a real problem. However, the whole process from the identification of the use case to the introduction of ML models in the company is not a trivial procedure.
In this hands-on workshop we will talk a little bit about machine learning basics and then we will leverage techniques like EventStorming and the ML Design Canvas. Event Storming is a method of Collaborative Modeling that helps technical experts, developers and all other project participants* to develop a common understanding of a business domain and thus identify possible use cases for innovative AI/ML technologies. Each potential use case is then formulated as an ML problem using the ML Design Canvas. Furthermore, the ML Design Canvas is used to structure the ML project and specify all components. We will also draw parallels to the Bounded Context Design Canvas and show how this approach fits into approaches like the model exploration whirlpool and / or the DDD Crew (GitHub.com/ddd-crew) starter modelling process.
- 10:00 - 12:00
- Online Event
- Domain-driven Design Europe 2021