Comment by Flashtoo | Hacker News Reader

Comment by Flashtoo | Hacker News Reader

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These are good practices to keep in mind when setting up GenAI solutions, but I'm not convinced that this part of the job will allow "data scientist" as a profession to thrive. Here's my pessimistic take.

Data scientists were appreciated largely because of their ability to create models that unlock business value. Model creation was a dark magic that you needed strong mathematical skills to perform - or at least that's the image, even if in reality you just slap XGBoost on a problem and call it a day. Data scientists were enablers and value creators.

With GenAI, value creation is apparently done by the LLM provider and whoever in your company calls the API, which could really be any engineering team. Coaxing the right behavior out of the LLM is a bit of black magic in itself, but it's not something that requires deep mathematical knowledge. Knowing how gradients are calculated in a decoder-only transformer doesn't really help you make the LLM follow instructions. In fact, all your business stakeholders are constantly prompting chatbots themselves, so even if you provide some expertise here they will just see you as someone doing the same thing they do when they summarize an email.

So that leaves the part the OP discusses: evaluation and monitoring. These are not sexy tasks and from the point of view of business stakeholders they are not the primary value add. In fact, they are barriers that get in the way of taking the POC someone slapped together in Copilot (it works!) and putting that solution in production. It's not even strictly necessary if you just want to move fast and break things. Appreciation for this kind of work is most present in large risk-averse companies, but even there it can be tricky to convince management that this is a job that needs to be done by a highly paid statistician with a graduate degree.

What's the way forward? Convince management that people with the job title "data scientist" should be allowed to gatekeep building LLM solutions? Maybe I'm overestimating how good the average AI-aware software engineer is at this stuff, but I don't see the professional moat.

I agree with your take.

I don't really see why evals are assumed to be exclusively in the domain of data scientists. In my experience SWEs-turned-AI Engineers are much better suited to building agents. Some struggle more than others, but "evals as automated tests" is, imo, so obvious a mental model, and can be so well adapted to by good SWEs, that data scientists have no real role on many "agent" projects.

I'm not saying this is good or bad, just that it's what I'm observing in practice.

For context, I'm a SWE-turned-AI Engineer, so I may be biased :)

I think there's a lot of methodological expertise that goes into collecting good eval data. For example, in many cases you need human labelers with the right expertise, well designed tasks, well defined constructs, and you need to hit interrater agreement targets and troubleshoot when you don't. Good label data is a prerequisite to the stuff that can probably be automated by the AI agent (improving the system to optimize a metric measured against ground truth labels). Data scientists and research scientists are more likely to have this skillset. And it takes time to pick up and learn the nuances.

I agree with you take the there isn’t a lot of specialist work for data scientists to do with using off-the-shelf LLMs that can’t be done by an engineer. As an AI-aware software engineer myself… this stuff wasn’t that hard to pick up. Even a lot of the work on the Evals side (creating an LLM judge etc.) isn’t that hard and doesn’t require serious ML or stats.

But aren’t there still plenty of opportunities for building ML models beyond LLMs, albeit a bit less sexy now? It’s not like you can run a business process like (say) AirBnB’s search rankings or Uber’s driver marching algorithms on an LLM; you need to build a custom model for that. Or am I missing something here? Or is that point that those opportunities are still there, but the pond has shrunk because so much new work is now LLM-related? I buy that.

> I agree with you take the there isn’t a lot of specialist work for data scientists to do with using off-the-shelf LLMs that can’t be done by an engineer.

Conversely, data scientists are doing software engineering, including webdev. It’s an interesting time. I think it’s less about the job title demarcation now, and more about output.

I agree. It is difficult to convince leadership to do this work at all ("it works on my example, ship it"), and in my experience most DS don't even want to do it.

One of the key value is that it forces some thinking about what is the task you want to solve in the first place. In many cases, it is difficult if not impossible to do it, which implies the underlying product should not be built at all. But nobody wants to hear that.

Doing eval only makes sense if making the product better impacts something the business cares about, which is very difficult to do in practice.

I think most use-cases will still use simpler models like XGBoost etc. rather than LLM's. Customer segmentation is a really common use-case with no need for an LLM. Same for revenue/LTV forecasting.

Perhaps they can use the LLM to write and deploy these models without needing a Data Scientist but that seems risky to say the least.

In my company, the most Data Scientist-adjacent people are the Data Analysts but they tend not to have programming experience beyond SQL and basic Python and they aren't used to using the terminal etc.

Do those use cases need LLMs? Probably not. but if good results can be had with a day of prompting (in addition to the stuff mentioned in the article, which you have to do anyway) and a smaller model like Haiku gives good results why would you build a classifer before you have literally millions of customers?

The LLM solution will be much more flexible because prompts can change more easily than training data and input tokens are cheap.

> Do those use cases need LLMs? Probably not.

One of the points of the article is the importance of gathering data to support your conclusions.

> prompts can change more easily than training data

Training data is real, and prompts are not. I don’t think this is an apples to apples comparison.

I don't disagree that very numerical tasks like revenue forecasting are not a good fit for LLMs. But neither did a lot of data scientist concerns themselves with such things (compared to business analysts and the like). Software to achieve this has been commoditized.

I don’t actually even know what people are hinting at when they say that LLMs replace the need for building custom models. Regression models? People are using LLMs instead of say building a Bayesian hierarchical model? That’s not possible. Time series modeling using an LLM? Also ridiculous. Recommender systems? Ok maybe, still utterly ridiculous and abysmally slow.

For anything NLP sure, it definitely wins. However, I’ve just recently used some big fancy OpenAI model to actually just label thousands of text data for me, just so I could build a classifier with CatBoost. Guess what, inference speed is at a guaranteed sub 100ms and it costs $0 in tokens. The”AI Engineer” solution here would be just run every classification request through an LLM.

AI Engineering is going to have the same problem we had when Data Science as a term arrived and you had every Statistician saying they’re just re-inventing everything that exists in statistics, poorly.

You're right. For years the real impediment to "AI" products at many companies was the sheer crappiness of ML frameworks which were built by and for grad students, not professional engineers.

When LLMs appeared it was just so much easier to use then as an uber model and leave behind the training and inference infrastructure (if you can even call it that).

Now that LLMs can code I expect we'll be coding up custom model pipelines more and more... but only when we stop subsidizing LLMs.

One thing data scientists brought to the table was statistical rigor in the models, but that seems to have left the building at this point with LLM-based solutions.

As a AI-aware software engineer currently creating systems that integrate with LLM provider APIs for my company- who also has no idea what an eval is or how a data scientist thinks about RAG. I honestly don't see what value a data scientist would bring to the table for my team. Maybe someone would care to enlighten me?

I believe data scientists and ML engineers should not be conflated.

I believe that the massive splitting of data roles over the past decade is both a product of ZIRP and premature optimisation.

So you consider data scientist akin to QA? They are just validating LLM based solutions?

You recognize that you haven't really needed strong mathematical (or coding) skills to create models for some time. Data Scientists add value by knowing how to translate business speak into XGBoost type model and interesting XGBoost model results into business speak. And, frankly, often by being some of the smartest people in the room. The math is occasionally helpful for speaking the language of the XGBoost model. And picking only people who are decent at math (and coding) helps ensure the smart factor. How much of that will really change with AI? I've also seen Business stakeholders try to use the chatbot to bypass the Data Scientist. Typically it's not long before there is a design decision or an interesting result the Business stakeholders don't understand. That's why I think there will be demand for Data Scientists. Not exactly evaluation and monitoring. And definitely not gatekeeping building of LLM solutions. Often the opposite, called in to explain and debug the Business stakeholders' slop.

  > You recognize that you haven't really needed strong mathematical (or coding) skills to create models for some time.

And then there goes something like this [1], where researchers failed to control for p-value: "In this particular setting, emergent abilities claims are possibly infected by a failure to control for multiple comparisons. In BIG-Bench alone, there are ≥220 tasks, ∼40 metrics per task, ∼10 model families, for a total of ∼10^6 task-metric-model family triplets, meaning probability that no task-metric-model family triplet exhibits an emergent ability by random chance might be small."

[1] https://arxiv.org/abs/2304.15004

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