How We Learned to Stop Worrying and Love Iterations

Machine learning can provide a fast track to solve hard problems and a proof-of-concept can often be created quickly. However, the way to production can still be long, since machine learning creates new challenges not encountered in conventional software engineering.

We are researching methods to automate critical text classification tasks in GfK’s data production since three years. To operationalize these models, our team builds and runs a set of interdependent services to be consumed by a legacy production system.

Frank Rosenthal and Laura Hoyden will talk about their journey and the lessons learned, focusing on how to balance data science, technology and agile delivery to achieve a sustainable business impact.

Vorkenntnisse

Basic understanding of machine learning and software development

Lernziele

• Understanding the challenges and being able to avoid potential pitfalls when building and rolling out machine learning solutions,
• Being aware of the issues and potential approaches to automate and stabilize the long-term operation of the machine learning solution

 

Speaker

 


Frank Rosenthal is Senior Data Scientist in the Global Data Science unit of GfK. He is leading the development and rollout of machine learning based components to optimize and automate GfK's point-of-sale data production process. He specializes in machine learning and data management.


Laura Hoyden is working as Data Scientist in the Global Data Science unit of GfK. Having a profound statistical education, she is using machine learning techniques to increase efficiency and automate text classification processes in daily routines at GfK. In addition, she is focusing on fostering research collaboration and the integration in the overall process landscape.

Gold-Sponsoren

HMS
Structr

Silber-Sponsoren

codecentric
Phytec

Bronze-Sponsor

incontext.technology GmbH

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