AI techniques like machine learning (ML) and deep learning (DL) offer new ways to optimize processes, automate workflows, detect anomalies, and reduce costs in manufacturing industries, with a potential value estimated at $2 trillion. However, these techniques can be tricky to implement and apply, with many potential pitfalls for the unwary.
In this webinar, we’ll explore some use cases for AI in manufacturing and highlight what data science and machine learning practitioners need to consider when applying AI to this field.
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Director of Engineering, Anaconda
Jim Bednar is the Director of Custom Services at Anaconda, Inc. Dr. Bednar holds a Ph.D. in Computer Science from the University of Texas, along with degrees in Electrical Engineering and Philosophy. He has published more than 50 papers and books about the visual system and about software development.
Dr. Bednar manages the open-source Python projects HoloViz, Panel, hvPlot, Datashader, HoloViews, GeoViews, Param, Lumen, and Colorcet. Before Anaconda, Dr. Bednar was a lecturer and researcher in Computational Neuroscience at the University of Edinburgh, Scotland, and a hardware engineer working on data acquisition at National Instruments.
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