We had a two day event last week with Data Scientists from various companies from Europe. We had about 50 people participating very actively. The focus of this event was to explore and understand, what it means to be a data scientist in an professional, economic environment. It became clear that this is much more than just the core Data Science disciplines, like working on Jupyter Notebooks to develop machine learning models or large analytics scenarios. This also means to work with several other roles like data engineers or application developers to make sure, the right data is available in a secure way, all compliance and internal regulations are met and finally, models can be applied in business processes and other apps to support the core business of the company. We discussed teaming and organizational aspects, ethical and compliance topics like bias, or GDPR, CICD pipelines with automation to run continuous delivery of new models into production, etc.
We had overview presentations and deep dives on IBM technology in the IBM cloud, mainly Watson Studio as the core tool for data scientists, but also the notion of the Refinery, Watson Knowledge Catalog, machine learning / deep learning services, the API economy that helps to deploy models into business application and the like. Our partner Telefonica Next presented their Data Anonymization Platform to allow use of former toxic data sets containing personal information in a GDPR compliant way through sophisticated anonymization of those personal information.
We heard, how the academic institutions deal with the new profession of a data scientist and got a glimpse about Quantum Computing and how it addresses the compute power problem in the future. We heard about a Kaggle challenge from a team in IBM and how it helps to boast skills in the area of machine learning.
Finally, we performed an ideation session based on our IBM Design Thinking methodologies to explore what the main challenges for Data Scientists in commercial environments are and based on those challenges, how this community can help in the future to address those challenges.
The presentations from this event can be found in the Library section of the Global Data Science Forum group in the community.
More to come...
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