Automated Machine Learning – do we need any more Data Scientists?

 
English Intermediate AI & Machine Learning

What is really "automated" here? Do we need any Data Scientists and what's our (I'm such a Data Scientist) role in this scenario. A short overview over all the libraries available so far (H2O, auto_ml, Azure AutoML, ...). How and why (?) does it work (giving an overview)? What is the difference between the different libraries and concepts? What is this thing doing exactly? Look at tools like Power BI, Azure ML Workspace, ... These methods left the early stage of their development and are becoming more and more mature. Many tools are already relying on the work of this algorithms. But is this AI? Really? We will see....

Speaker

Mario Schnalzenberger

Data Scientist, cubido business solutions GmbH

Mario Schnalzenberger ist seit fast 25 Jahren in der IT-Industrie tätig und setzt sich seit über 15 Jahren im Bereich Statistik und Big Data als auch im Predictive Bereich mit vielseitigen Lösungen auseinander. Seit fünf Jahren ist er im cubido-Team und unterstützt die Kunden in diesem spannenden Umfeld. Mario hat Abschlüsse in Statistik, Volkswirtschaftslehre und Sozial- und Wirtschaftswissenschaften. Er unterrichtet(e) an mehreren Universitäten und Hochschulen in Österreich sowie der HTL Leonding. Er trägt regelmäßig bei verschiedensten Konferenzen in den vielfältigen Themen der angewandten Statistik, Informatik, IOT und AI vor.

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