Michael works as a Fellow for INNOQ in Germany. He has over 15 years of practical consulting experience in software development and architecture. His main areas of interest are currently Domain-driven Design, Microservices and in general Software Architectures. Michael is a regular speaker at national and international conferences.
Michael Plöd, Larysa Visengeriyeva
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.
After the workshop the participants will:
- Understand how to dissect a domain with Event Storming.
- Understand how to find AI/ML Use Cases using EventStorming - Learn how to structure AI/ML Use Cases with the ML Design Canvas
- How to conduct AI/ML EventStorming workshops for your products
In various communities, several methods for the collaborative modeling of business requirements have been established in recent years. Well-known examples are EventStorming or Domain Storytelling. These approaches are based on achieving a better shared understanding of the business requirements in an interdisciplinary way. But what about the requirements for the quality of the software being developed?
This is where Quality Storming comes in, trying to bring together a heterogeneous set of stakeholders of a product or project to collect quality requirements. The goal is to gain a shared understanding of the real needs for the quality characteristics of a product. To achieve this goal, Quality Storming uses some techniques from various already existing collaborative modelling approaches.
It is not the claim to produce perfectly formulated quality scenarios with the help of Quality Storming. Instead, the method aims to create a well-founded, prioritized basis for later formalization, which is understood across different stakeholder groups. The more often teams work with the technique, the better the quality of this basis becomes over time. Advanced teams are quite capable of creating very well-formulated scenarios within the framework of such a workshop.
In this workshop I will introduce the approach and do a quick hands-on Quality Storming with the participants. The workshop will consist of 20% explanations (slideless) and 80% hands-on work in Miro. We will also take a look at how the learnings of a quality storming workshop can be fed into the bounded context design canvas.
Since DDD Europe is an online only event in 2021 we need to get creative regarding socializing formats and let's be honest: remote beer drinking and (ordered) pizza eating is a bit boring and not very engaging. So let's go with an interactive way of socializing by cooking a simple but delicious vegan dinner together.
The session is something like an interactive TV cooking show and works as follows:
- Michael provides you with a purchase list of ingredients no later than one week before the session. Don't worry: we'll have a dish with very common and easy to get ingredients.
- We will all ramp up our webcams in our kitchens and Michael will guide the whole group through the preparation of the recipe. He will make sure that everyone can follow along nicely.
- After the dish is ready we will have dinner together in a remote way.
You do not need to be an advanced cook to join, basic kitchen skills are sufficient.