I think most of those things will remain important:
+ Designing the network architecture is a means to instill your knowledge of the problem into the network. For example, using convolutions over images encodes some translational invariance into the network. It makes up for lack of data. I don't think data augmentation alone is enough, either: if you use a "stupid" architecture with heaps of data, the computation will become too expensive or slow.
- The systems engineering part will probably get automated. I bet there are Amazon engineers crying at their desks while working on AWS Elastic Tensorshift right now. So unless you're specifically interested in that side of things, maybe this isn't the best area to focus on.
+ There are always going to be problems, so knowing how to debug is a useful skill.
+ ML/stats fundamentals aren't going away. You need to know what you're trying to do before you can do it.
+ Designing the network architecture is a means to instill your knowledge of the problem into the network. For example, using convolutions over images encodes some translational invariance into the network. It makes up for lack of data. I don't think data augmentation alone is enough, either: if you use a "stupid" architecture with heaps of data, the computation will become too expensive or slow.
- The systems engineering part will probably get automated. I bet there are Amazon engineers crying at their desks while working on AWS Elastic Tensorshift right now. So unless you're specifically interested in that side of things, maybe this isn't the best area to focus on.
+ There are always going to be problems, so knowing how to debug is a useful skill.
+ ML/stats fundamentals aren't going away. You need to know what you're trying to do before you can do it.