Many achievements of Machine Learning have been possible only by using specific hardware or distributed approaches. As with the ML algorithms, there have been steady improvements, some of which are game-changers. In this presentation I will outline the status and outlook of the following approaches:
- Neuromorphice Engineering
- GPU accelerated ML
- Cluster Machine Learning, e.g. Apache Spark
- Quantum Computing
Python snippets for each technique give a hands-on starting point.