The whole thing feels dishonest and misleading. Welcome to the industry :p Here are some of my thoughts: "Machine learning" and "data science" are both incredibly poorly defined, very broad terms meaning often depends a lot on the context in which these terms are being used. I'm personally of the opinion that a better phrase for most of what we call "Machine learning" is "statistical learning" and subsumes basically all of predictive analytics. In other words, if you're model is just a simple linear regression: I still think it's ok to call that "machine learning", although I think it's also fair to maybe characterize that as somewhat dishonest since the phrase obviously conveys a notion of technical sophistication. increasingly, the phrase "Machine Learning" is becoming analogous with "Deep Learning," but this absolutely is not correct. The vast majority of applied ML in industry uses techniques like GLMs and tree ensembles. Ultimately, this is all marketing speak anyway and people will use the language that maximizes hype for whatever they're trying to pitch to you. Speaking from my own diverse experience as someone who's worked as a data professional in a wide diversity of organizations (including two FAANGS), you'd be surprised how unsophisticated a lot of real world "data science" is. Most, even. most data scientist roles are at organizations that are undergoing a "data transformation" to become more "data driven". This is code for "historically, we made most of our business decisions based on intuition and maybe sticking a finger in the wind. the consequence of this is that data scientists get buried in "low hanging fruit" opportunities because there's so much opportunity for low-effort improvements by minimally attending to data, the data teams and the organization at large are heavily incentivized to leverage simple, unsophisticated solutions so they can tackle more problems quickly rather than heavily optimizing solutions to narrow problems. the value of sophistication is really a function of scale when I talk about "low hanging fruit", I'm talking along the lines of: domain expertise gets you say 50% of the available value for some opportunity, data-informed busienss rules takes that up 70%, simple modeling takes that up to 80-85%. So by tackling the low hanging fruit, you've captured close to 70% of the available additional value with very little effort because that last 15% optimization isn't low hanging fruit, we're going to quickly encounter diminishing returns. Every additional percentage point of optimization is going to come with exponentially more effort. This is the reason the bulk of data scientists are employed by huge companies like FAANGs: the scale of their business is large enough that an incremental improvement of a fraction of a percent can mean millions of dollars in revenue or savings. conversely, if your organization is not operating at that scale, it's not unlikely that it'll will cost your company more to invest in optimizing some solution than the value they would get from that solution. And even after putting in that investment, it's still a huge risk. Every application of predictive analytics is essentially a kind of experiment, and with every experiment, there's a possibility that the tested hypothesis is wrong and will be rejected, i.e. the model doesn't do anything of value. When data scientists are given the freedom to do their best work, they are and need to be a huge cost center for whatever organization they operate in. Otherwise, you're asking them to essentially be be data-savvy business consultants who are perpetually chasing low hanging fruit, which is exactly the position most industry data scientists find themselves in. Because of these scaling effects, most of the work that actually does require sophistication will end up getting subsumed by engineering teams if you are a data scientist working in isolation, your ability to operate on large data sets and deploy complex models is limited. This type of work requires engineering support, which means the further away your data scientist is in the org tree from the closest engineer, the more their hands will be tied with respect to the amount of sophistication they can apply to anything that will be deployed. deep learning and data science tools are rapidly becoming staples of undergraduate CS curricula, which means more engineers are equipped to identify and act on opportunities to apply ML without engaging with a data scientist. this creates a kind of feedback loop that further isolates data scientists from the engineering resources they need, often relegating them to being a kind of "ad hoc analytics monkey" for leadership. the "value function" the solution here is optimizing is probably more multi-faceted than you realize specifically, even if your data scientist has all of the engineering resources they could want to deploy the most sophisticated SOTA solution to your orgs problem, there might be good reasons why they wouldn't want to. the ultimate goal of these sorts of projects is almost always to drive some kind of behavior. This often means that it's more important for the outputs of a model to be interpretable than predictively accurate. additionally, the data scientist is ultimately subject to the demands of their customer: the business stakeholder. This unfortunately means that sometimes they will be relegated to approaches whose mechanism can be understood by the stakeholder, especially if it's a new relationship and the data scientist is still building trust in the org. It can even mean the scientist will be required by their client to incorporate features in the model that don't carry any predictive signal at all. This creates an even heavier bias away from sophistication than the whole "low hanging fruit" or "I'm my own data engineer" thing. First and foremost, the data scientist is doing work for their customer and they need to make their customer happy as best they can. TL;DR: From what you've shared, I see nothing inappropriate about describing this work at least as "data science". Calling it "ML" carries a weak implication that deep learning or something similarly sophisticated is being used, but even if that's not the case: the fact that they're forming predictions of any kind by performing computations on historical data I think makes it appropriate. More on reddit.com

ML programs are fitting parameters of a model to make a generic thing do a specific thing. "Classical" programs are just programmed specifically to do the specific thing. Take something simple enough to do it easily either way: compute exclusive or. Here’s a classical version. bool xor(bool a, bool b) { if((a && !b) || (!a && b)) { return true; } return false; } You can also train a neural network to do this. I’m not going to write all that code, but I’ll explain the concept. A neural network has nodes and edges. You have one node for each input (here I have two inputs, an and b). There are additional nodes downstream from the input layer connected to the input nodes by edges, and edges have weights. To compute the output, you feed each input to one of the input nodes, multiply the input by the weight of each edge coming out of that node, and then sum up all those multiplications and apply some threshold, and that gives you a computed value at each node. You keep doing that through all the connections u til you get to the last node in the network, and it spits out your answer. There’s a lot going on that I glossed over. Here is a more detailed explanation. https://towardsdatascience.com/how-neural-networks-solve-the-xor-problem-59763136bdd7 I said this can solve the xor problem…how? Well, let’s let 1 be true and -1 be false. I feed my inputs (an and b) into those input nodes, do all my multiplications and additions and thresholds, and if my last node outputs 1 or -1, that’s my answer. But will it compute the right thing in all cases? To make sure it does, I train it. I give it examples with the correct answers, and let it calculate. If it’s answer doesn’t match the right answer, I change the weights on those edges in particular ways that eventually make the errors go away. The ML program is the program that changes the weights to make errors go away. I as the programmer am not thinking about exclusive or. I’m just thinking about training data and errors. If you know how to just write the program, it would be silly to use ML. It’s way harder and slower to like determine if an array is sorted by training some ML method to try and fit parameters to a model than to just write the code to check it. You use ML when you don’t know what else to do. Suppose I give you a bitmap and ask you to compute whether it contains a picture of a squirrel. bool has_squirrel(bitmap b) { for(int row=0; row More on reddit.com

this is pretty much how tech has always worked, and i say this as someone with more than a decade in dev/ml engineering. there is always churn in skills and massive hype cycles, gotta get used to this. anyways, the fundementals are not wasted. understanding backprop and gradient descent means you'll actually grok why fine-tuning works and when it'll fail spectacularly. the people who are capable of doing api calls only are gonna hit walls you wont. also hot take: we're in peak hype cycle right now. half these genai internships are gonna be building things that get quietly sunset in 18 months when someone realizes their "ai-powered solution" could've been three if statements. a lot of execs and hiring managers right now are incentivzed to get to market "ai-powered solutions" traditional ml isn't dead, it's just not sexy rn. computer vision, fraud detection, recommendation systems, demand forecasting, anomaly detection all still running on "boring" ml at massive scale. those jobs exist, they're just not flooding linkedin because they aint the hot new thing. the real skill is learning to surf hype cycles without drowning in them. pick up the genai stuff (it's legitimately useful), but don't burn your fundamentals notes. More on reddit.com

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