It is all-to-easy for someone who is just interested in the results to look for and use existing code that can help with the solution. The higher-level the better. This raises the problem of tying one’s future to a specific set of techniques. In this talk we will look at implementing Machine Learning solutions which take advantage of the great innovations in hardware without binding ourselves to a specific technology which might go out of fashion or gets abandoned.
Required audience experience
General audience but developers will appreciate more details
Objective of the talk
To make clear that bridging the gap between the great pieces of hardware, bought or produced (FPGA), and an ML solution does not and should not require software that cannot be controlled fully. Proprietary stacks or abandonware can and will cause problems. Judging risk vs cost is needed.
You can Ulrich’s slides and presentation below: