Workshops

After the workshop, you will be able to bring DataOps to your projects and to your organization and generate more value from your data. You will have learned about DataOps values and principles and how to apply DevOps, Agile, and Lean approaches to your data projects. You will have learned about the important role of company culture, stakeholder management, team composition, and data governance. With the DataOps Radar, you will have access to a tool that allows you to discover the potential in your own projects – and you are going to leave the workshop with actionable ideas.

MLFlow is one of the most popular tools to manage the machine learning lifecycle from the beginning to the end. In this one-day workshop, we propose to guide you through the various ways you can use this platform to track, package, deploy and share your models.

The 21st century is shaped by the ever-increasing use of data for getting new insights and making better decisions. The center of such applications are data-based prediction models. More often than not, these systems do produce unintended discrimination and social injustice, a phenomenon which has been called “algorithmic bias” or “algorithmic fairness”. Newly built tools and the ones already in place today are both affected. This has triggered European Union lawmakers to develop and publish a proposal for a Regulation on Artificial Intelligence (AI) earlier this year, which requires algorithmic systems to avoid such biases. In addition to that, there is an increasing awareness of society and customers regarding the social implications of data-based systems. More and more, these systems are expected to be fair, non-biased, and non-discriminatory.

Data science and artificial intelligence are the key to successful value creation and competitiveness for many organizations in the future. However, to fully exploit the potential of these technologies, it is not enough to invest in tools and know-how alone. To create real innovation, one must combine these new technologies with industry expertise and integrate them into the operational business. We will discuss how to establish a data mindset in your organization.

Der Workshops wendet sich an Praktiker und Entscheider, vornehmlich aus der produzierenden Industrie. Physische Produkte, Produktionsverfahren oder Dienstleistungen, die mit physischen Objekten in Verbindung stehen, sollten eine wesentliche Säule des Unternehmens sein. Es soll also um «Hardware» gehen! Wir zeigen ganz konkret den Stand der Technik auf. In unserem Vision-Lab werden wir demonstrieren, welche Möglichkeiten die Bildverarbeitung, neue Sensorik und Algorithmik bieten. Der Besuch an einem Automatisierungs-Prototyp zeigt, was Predictive Maintenance im praktischen Einsatz bedeutet. Damit werden Bildverarbeitung, vorausschauende Wartung und die künstliche Intelligenz erlebbar.

This workshop will provide an introduction to MLOps. After attending the workshop, the participants will have gained an understanding of MLOps principles and will have applied these hands-on to an end-to-end data science project using real-world data and state-of-the-art frameworks.