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ML/DL is past the peak of inflated expectations and in the trough of disillusionment.
It’s also becoming an engineering problem and not so much of a research problem. I can go on AWS right now, train & deploy a rnn and get predictions by the end of the day. I also don’t need a PhD to do it.
All I need is a data engineer with cloud skills and an up-skilled data analyst to do it.
This
Where do you see this happening? Where I work there’s a lot of what P1 has mentioned. People hiring have no idea what data science is. They think machine learning is something just out of this world. If you hire someone that knows stats, but is clueless about the business side, there’s a big communication gap.
High pay, no associated revenue stream = overhead
Spot on
This was long overdue, and will only pick up steam as time goes on.
There are two issues. First, there are many people who are merely posing as experts in machine learning — a 15 minute conversation with someone on Bayesian stats is pretty illuminating here. Second, consulting companies are only able to recruit the bottom of the barrel — and worse, the people doing the hiring at consulting firms are not the most technically or mathematically inclined (is MBAs).
For folks with solid backgrounds and true expertise in the math, there may be minor hiccups, but the market will still stay strong.
I don’t think a normal consultant knows enough to properly handle data analysis. People are basically clueless when they need go beyond descriptive stats and some can’t even do that properly. You’re mixing up skills here.
Which firms are laying off Data Scientists and CS/ML folks? Maybe those people have the label without the skills?
Academics tend to be pretty bad at consulting.
Stick to people with highly applicative skillsets.
Yeah, pretty much. Turns out data isn’t necessarily the magic solution to literally everything. And companies are catching on.
If this is how you do discussion, people must LOVE talking to you!
Clients don’t want to buy, and when they do buy the don’t want to implement. Been on projects where we performed statistical analysis that went against clients preconceived notions of their business data and they said “nobody will want to believe this and action on it”
What PhD’s are working on problems that are market ready?
Milk it until AI/ML goes through another winter.. Jumps on the next bandwagon.