There is a lot of visioning going on around AI/Augmented/Automated machine learning as well as some first good specialist topic examples. But what are the techniques being used and can they be applied to day to day tasks? This presentation will bring together the latest thinking on practical automated machine learning and apply it to a common task: ensuring relevant graphics are created. This is of course useful for non-data science professionals but has a particular value for data scientists who are taking an initial look at new very big very wide data to determine an approach. A summary of the status quo will be given along with practical open source examples to show the techniques and concepts in use.

The excitement about the potential opportunities for leveraging data by means of advanced analytics is huge. But, the honeymoon between business and data science is over. Stakeholders want to see value generation from data science. At Roche Diagnostics the Data Science Lab was created. Its mission is to explore business opportunities for data science across the company and to deliver productive, algorithm based systems that create impact. In his keynote, Frank will present some examples of data science initiatives going from data exploration over predictive modelling to productionization. Some of the challenges encountered will be addressed as well as the learnings.

Customer Data Platform (CDP) systems are the newest answer to an old question: how to assemble a complete view of each customer. This session explores the reality of what CDPs can and cannot do, how CDPs differ from other systems, the types of CDP systems available, and how to find the right CDP for your purpose, especially with regard to data science projects and predictive modeling. You will come away with a clear understanding of where CDP fits into the larger data management landscape, what distinguishes CDP from older approaches to customer data management, and the state of the CDP industry in Europe.  

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