I plan to enter a PhD program in 1-2 years to specialize in ML/Deep Learning. Assuming it'll take 5-6 years to complete my degree how applicable should my skill sets be in industry at that point?
I do have one suggestion: learn to handle dirty data.
I work with ML researchers and notice two things: they're pretty bad software engineers (no knowledge of software patterns, bugs galore), and they almost never know how to clean their data. The latter is because they do a lot of their research using pre-cleaned, standard data sets. You never get that in industry.
as to ML, its adoption is hyped. it is powerful, but not as anyone really talks about.
support vector machines and Bayesian learning have been around since the 70s/80s (ninja edit: SVM's since 1963! Markov Chains 1950s, Bayesian Learning/Pattern recognition sine the 1950's), but adoption has been slow due to the nature of business, which is now drooling over it since neural networks beat a few algorithms.
due to the hype, more business will opt for ML now, but the craze will plateau and ML will become another tool in your arsenal.
so basically, you really have nothing to worry about - use your Phd to do interesting things, come up with novel and new research and/or develop your own product.
don't let your job security worries get in the way of enjoying what you want to do now, you're already good and in STEM (and if you don't feel good enough, work on yourself until you do).
This is one of the things I find hardest about convincing managers and leads of. They think things like CRFs and Markov models are "new" methods and too risky. So they opt for explicit rule-based systems that use old search methods (e.g. A*, grid search), which hog tons of memory and processor. Those methods rarely ever work on interesting problems of the modern day.
They can understand the rule-based methods easily. They have a hard time leaping to "the problem is just a set of equations mapping inputs to outputs, and the mapping is found by an optimization method."
The people who can't hack it are those who'll be in trouble. Tech has never much been the place where credentials are necessary.
Don't specialize and saddle yourself with years of college debt if you're unsure of the field's long term prospects.
And companies like Google are pretty keen on academic credentials. They've assembled what must be one of the largest collections of PhDs in history.
Right. The debt problems that people have after PhDs are more often due to their undergrad loans sitting around for 4-7 years while they were earning enough to subsist and not more.