I have an almost opposite problem. I spent years learning alot of ML stuff and worked at a job doing this kind of work for a couple years or so. I think the issue was that the data we had at the organization and the internal politics seemed to make it difficult to use for ML in a way that mattered to the business. I grew frustrated with having spent alot of time learning things that were exciting then realizing it didn't really matter if some manager can just say "we're doing it this other way that makes sense to me." (Not based on data, but gut feelings)
I'm not sure what to do with that. Probably ML works best in organizations and situations that are on board for using ML to make decisions for the business. Here's the other thing -- finding a business where ML is core to its decision making that will hire a person with no formal ML related education may be difficult. Perhaps I'm wrong about that and have just given up on ML after my frustrating experience.
Now I'm building data systems that the business uses on a daily basis to get things done. I feel alot better doing that than ML stuff, even though I loved playing with data and ML. I guess I've given up on ML for now, maybe I'll find my way back to it again.
I agree. Most of what I've seen suggests that everyone _thinks_ they need and want machine learning specialists. But mostly they need people who are flexible in how they combine business acumen with statistics, a little ML, analysis, and programming. Business owners usually insist they need the ML soooo much, but they're rarely willing to go all the way and actually deploy ML. Plus, often they don't really need it...Or even modeling of any sort. They may need automated dashboards (for keeping an eye on important KPIs), decision support platforms, etc... Lots of things which require something more than analysts, statisticians, programmers, and the like. In essence they need highly capable jack-of-all-trades who can, on occasion, bring to bear advanced algorithms and stats. But that's a lot more rare than you'd be led to believe given job postings and all the news articles.
I'm not sure what to do with that. Probably ML works best in organizations and situations that are on board for using ML to make decisions for the business. Here's the other thing -- finding a business where ML is core to its decision making that will hire a person with no formal ML related education may be difficult. Perhaps I'm wrong about that and have just given up on ML after my frustrating experience.
Now I'm building data systems that the business uses on a daily basis to get things done. I feel alot better doing that than ML stuff, even though I loved playing with data and ML. I guess I've given up on ML for now, maybe I'll find my way back to it again.