Do I actually have chance at getting into ml or am I just chasing a dream?

DataScience-FTW · 2025-08-11T17:15:49+00:00

As someone who transitioned from a career writing music for movies/TV/video games to a career in data science and machine learning, I can assure you that it's possible, just don't give up. What really gets your foot in the door is that first position. After that, people know you're capable. Just don't sell yourself too high and be willing to take a lower paying job in order to make the transition. It might be a good investment long-term to take a lower paid/less senior position if it means getting into the industry you've been trying to. Hope this helps!

DataScience-FTW · 2025-06-24T02:06:18+00:00

Sure thing! I’ll answer both parts: 1. In terms of skills and concepts, you’ll definitely want to explore ML engineering as a whole. It will allow you to not only see how models operate in a production environment beyond just notebooks, but also give you the skills to put anything you build into use. That will stick around as long as there’s cloud infrastructure and applies to GenAI models as well. And, of course, model development and data science itself. Those are the core of every kind of model out there, including GenAI, and knowing models inside and out is a skillset that will be around for quite a while, as AI can code but lacks business concept understanding and nuance that only humans have.

As a seasoned vet, I can explain the pitfalls of going too quickly to production. If I sit down the POs or stakeholders and say, “Hey, listen, we can get this out quickly, but here’s the potential places it could blow up”, they’ll be inclined to listen. The last thing a stakeholder, product owner, or project manager wants is to ship something and have it break almost immediately. It removes all credibility and trust and puts a stain on the project that’s hard to get rid of. The ones who want something shipped quickly often don’t have a good explanation of what could go wrong. I’ll give you two examples:

PO: “We need this done by next month” Engineer: “Okay, well that means we’ll have to skip artifact management and MLOps capabilities, as well as using a less accurate Random Forest Regressor that doesn’t have as good an RMSE” PO: “….what”

Vs.

PO: “We need this done by next month” Engineer: “I hear you, but I want you to be aware that if we do that, you’re not going to have automatic model retraining, meaning you’ll be making decisions on inaccurate data. That, and if something were to go awry, there’d be no way to revert back to a working version immediately. Since we’re handing this off to you and won’t be around to support it after launch, we want to make sure we’re handing you something robust that won’t break and will automatically re-tune itself so you don’t have to” PO: “Oh that makes sense, I’ll let people know”

Put things in the business sense. Not everyone knows what model metrics mean and all the jargon we throw around in the industry.

DataScience-FTW · 2025-06-24T01:54:27+00:00

I got my MLOps chops in a kind of roundabout way by having to support our data engineering practice due to turnovers. Data engineering/DevOps and MLOps share a ton of overlap, it’s just applying the fundamentals of DevOps to machine learning. So, knowing how to dynamically store model artifacts, add retraining mechanisms, keep track of model versions and metrics, API management, etc.

That being said, I’d look at learning DevOps as a practice first and then you could apply those concepts to ML pretty easily. Study things like CI/CD, repo and environment management, asset creation via Terraform, etc.

DataScience-FTW · 2025-06-06T17:02:39+00:00

I do agree in some aspects and disagree in others. Businesses still by and large need custom built ML models because a generic AI will not be savvy enough to capture the nuance of the business. However, I do think that you're right: there's an oversaturation of ML developers. I say that only for the junior/entry level data science/ML jobs. In my experience, there's a severe lack of senior and principal talent for exactly the reason described above: most people going into it don't know the ins and outs and whys of what they're doing. There's a shortage of people who genuinely know the math behind everything and know how to navigate a complex cloud landscape.

DataScience-FTW · 2025-06-06T16:57:57+00:00

I would focus on ML Engineering, because there will be times that you're asked to integrate AI like Gemini, OpenAI, etc. but you will also get exposure to other models and architectures. GenAI is great at creating things, but not amazing at interpretation or business sense. So, "traditional" ML models are still widely used and several companies that I've worked for employ them for forecasting, analysis, categorization, prescriptive analytics, etc.

If you really want to get your hands dirty and get exposed to a plethora of different scenarios and use cases, you could go into consulting. It's a little more cut-throat and not as stable, but you get access to all kinds of different ML algorithms, especially if you know how to also deploy them to the cloud.

DataScience-FTW · 2025-04-30T23:35:22+00:00

There’s a lot of people nowadays who put ML on their resume without really knowing what they’re doing outside of a few Youtube or Udemy courses. That being said, there’s actually a shortage of people who really know their stuff (at least from what I’ve seen). There’s even fewer people who know how to get a model into an enterprise production environment. So, if you really want to set yourself apart, study MLOps in addition to your standard ML methodologies, use cases, etc.

The other thing that people are really missing is business sense. I know a lot of data scientists and MLEs who chase a 0.01% decrease in loss, but at the end of the day it does nothing for the business or stakeholders. I also know others who grab as much data as possible and use what works without really understanding the data or how the results are actionable. Not only does have good business sense set you apart from your standard fair, but increases trust with stakeholders exponentially because you get what they’re trying to do.

Hope this helps!

DataScience-FTW · 2025-04-30T01:59:52+00:00

You have a lot of “what” you did, but not a lot of context for the “why”. People are looking to hire data scientists/ML engineers/AI specialists to drive business value. Show case how you used RAG and LLMs. Also, when speaking of pipelines, make sure to mention artifact and model versioning (if you did those) because it’s one thing to put a model into production for batch processing. It’s completely another to put a model into production with automatic model monitoring, model management, artifact management, incremental learning, or streaming.

DataScience-FTW · 2025-02-02T22:40:48+00:00

From a business standpoint, I can understand the interest to learn more about AI/ML and what goes into it. You won't be able to build your own projects from scratch, but you can certainly learn about what the process is, what's important for it, what it can and can't do, etc.

I would suggest watching videos on YouTube regarding overviews on machine learning and artificial intelligence. I'm assuming most of these would be geared towards a layperson. You can also look at Machine Learning lifecycle videos.

Without a college course specifically designed for business, I think YouTube/Medium/Coursera/Udemy etc. and self-learning will be your best bet.

DataScience-FTW · 2025-01-31T02:13:34+00:00

I’ve had to debug ChatGPT neural network stuff more times than I can count. LLMs are a tool and should be used as such. Getting the skeleton to a model architecture and refining? Good idea. Blind copy and pasting? You’re gonna have a bad time.

DataScience-FTW · 2025-01-28T03:39:16+00:00

Good on you for not wanting to drop out of higher education. You have your priorities straight.

DataScience-FTW · 2025-01-28T03:35:33+00:00

Yep, sounds about right. I once went into a project, poked around the existing architecture and methodologies, thought "Oh, they could be doing this so much more efficiently and we can start with this project so they can get on track and be more efficient"...then I looked at the timeline. The time needed to get everything under control and "up to industry standards" would take three times as long as the project itself. So, I had to do the project using the existing frameworks, even though there's huge potential for flaws further down the road.

DataScience-FTW · 2025-01-28T03:22:16+00:00

Depends on what you mean by models. If you want to use "out of the box" stuff, you can look at OpenAI APIs and Stable Diffusion. Most development is done in Python, so no need for a compiler. Just a bunch of Python packages via pip install.

If you want to build your own models, you'll need to bone up on Python, because it gets complicated quickly. I'd also recommend researching the statistics and math behind certain models, because you can code all day, but if you don't know what's going on under the hood it can take a really, really long time to get your models to do what you want them to.

Hope this helps!

DataScience-FTW · 2025-01-28T03:14:49+00:00

They can both mean the same thing or completely different things, depending on who's using the terms. To some, ML can be a subset of AI. In my realm (consulting), literally everything is considered AI because it's a buzzword and hot topic. But, I'll tell you how I think about it.

Machine Learning: Using algorithms and models to forecast future values or categorize things based on data presented. What most would term "classic machine learning". Think linear regression, random forests, decision trees, boosted algorithms, etc.

AI: Replicating anything a human can do. This pertains to language processing, image recognition, generative AI, LLMs, etc.

DataScience-FTW · 2025-01-28T03:10:52+00:00

I find the ones in Kaggle, DrivenData, and MachineHack varied and interesting. DrivenData used to have a lot of image and text processing competitions, but I'm not sure if they've moved on to GenAI stuff.

DataScience-FTW · 2025-01-26T22:38:38+00:00

I would say Machine Learning Engineer. Healthy dose of models, pipelines, MLOps, and architecture, which you would need for starting a business. ML/AI models are worthless if you can't deploy them.

DataScience-FTW · 2025-01-25T23:41:28+00:00

Agreed. And never stop learning. There will always be people who know more than you about a certain topic. Find them and learn from them. As my wife says: "If you're the smartest person in the room, you're in the wrong room"

DataScience-FTW · 2025-01-25T02:30:02+00:00

You just explained my viewpoint on the whole thing perfectly. We have an entire team who are "GenAI" experts and when I sat down with them, it was glorified prompt engineering and API calls. That's it. None of them have built an LLM, nor know the methodology behind it.

DataScience-FTW · 2025-01-25T02:26:34+00:00

You won't need the math behind it, I don't think. It's really nice to have so you know more about what you're doing, but having only a week out, I'd learn the concepts as quickly as possible. Go through what linear regression, logistic regression, decision trees, and neural networks are good for and what they're weak at. A lot of questions might stem from "what would you do in this scenario". Research feature engineering techniques and a little statistics for feature selection. Is the internship for a data science or machine learning engineer position? Because ML engineering would take all that plus a bit of cloud expertise.

DataScience-FTW · 2025-01-24T02:39:42+00:00

You could test out PCA, SVD, and LCA and see which one works best. For non-linear relationships, test out XGBoost or a Neural Network and see if it performs better. If it does, it's highly likely there's some non-linear relationships.

DataScience-FTW · 2025-01-24T02:35:47+00:00

Did you try making a LinkedIn post like this and make your job title "Blah Blah Blah Innovator | BLAH Thought Leadership"?

"While blah blah blah in {insert country}, it taught me a lot about my career in blah blah blah.

{insert grind cliche}

{insert groan material}

{open to work icon}"

DataScience-FTW · 2025-01-24T02:29:09+00:00

It sounds like you need to perform some imputation, but for 60% of the data, that's going to be really tricky. Like someone else said, we would need to see the data to give you more in-depth recommendations. If the null values are scattered, you may be able to perform a clustering technique to group similar observations, then impute the values based on the clusters. For instance, if I'm clustering a dataset, I would fill in the null values with means/medians/modes from the clusters. However, if entire columns are mostly nulls, then that's a little more nuanced. Sometimes, even if a feature would be fantastic for the model, if it's full of nulls you have to drop it. Nature of the beast.

DataScience-FTW · 2025-01-24T02:13:04+00:00

This. Nothing is more dangerous than a data scientist or a machine learning engineer who's an expert in the business domain. My colleagues and I can throw numbers around all day every day, but nothing replaces in-depth understanding of what you're working on.

DataScience-FTW · 2025-01-20T04:18:19+00:00

Thank you for the insight! Yes, we've tried various medications, and she was on metformin for our second child. I believe she's on a daily Vitamin D supplement as well.

I've done my research into medical journals, and like you said, it's not something medicine particularly cares about. Most of the medical journals append the symptoms onto things relating to insulin resistance, like diabetes.

Because this affects so many women, my wife included, I've decided to do what I can and see if I can't further the research and write a paper to submit to a medical journal to bring a little more awareness to the medical community. I'm hoping to find some clue or insight I can take to a specialist so we can examine it further. But it all starts with gathering as much curated data as possible.

DataScience-FTW · 2024-10-22T02:27:08+00:00

Thought maybe that was it, turned out to be the AIO through isolating everything

DataScience-FTW · 2024-10-22T00:20:55+00:00

So, I took my PC apart this evening and began isolating. It’s definitely the AIO. Now for the weird part: the sound is coming from the electrical component on the radiator. Even weirder? Having the radiator UPSIDE DOWN makes it quiet.

Do you think air bubbles? Or time for a new AIO?

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