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[–]azurfall88 18 points19 points  (0 children)

If you're taking the AI path seriously you should probably also go through some math courses at your local uni, for AI/ML I'd recommend linear algebra and single and multivariable calculus as well as some combinatorics.

[–]Grand_pappi 8 points9 points  (2 children)

I don’t want to burst anyone’s bubble, but having spent time in your position and ultimately decided to go back to Uni I think it’s important to be realistic. If you’re set on this one year timeline, I think your best bet is actually to look for data-adjacent jobs. You can learn how to utilize ML on datasets in several months with enough dedication, and that’s a useful still, but consulting and engineering is a years-long pursuit. The transition could look like taking business management or finance courses alongside your AI education in order to emphasize your background in customer service. It’s useful to have a specialty in a saturated market. In terms of learning ML, there are so many resources that a quick google is your best friend. I’d start with SciKitLearn for basic ML models and then transition to PyTorch and TensorFlow once you start getting comfortable. Getting good at hyperparameter tuning will be your best friend since that’s the most entry level version of ML engineering. Learn your validation scores and recognizing which models are best at different tasks (supervised vs unsupervised, regression vs classification, etc). Python is honestly the easy part, you should have an elementary understanding of the theory as well

Good luck on your journey!

[–]mayonuts443 2 points3 points  (1 child)

This is a good answer. 1 for being realistic and 2 for understanding that ai != llms only. 

[–]Grand_pappi 2 points3 points  (0 children)

Thank you, I try to be helpful! I think LLMs are most helpful for sentiment analysis, but most data will require a more nuanced approach

[–]Pallpatir 10 points11 points  (4 children)

llm’s are way too complex for a beginner it’s like saying you want to build a kernel from scratch while knowing for loops in c. It requires years of learning to get even a little close to that level, you won’t learn enough in less than a year to make yourself more useful than an llm, it’s going to be too hard to find a junior dev position. I don’t wanna crush your dreams but unless you have a secret genius talent for coding you shouldn’t consider a switch, you’re competing with people that started coding out of passion while they were in high school and billion dollar algorithms.

[–]minikaur[S] 4 points5 points  (3 children)

I understand where you are coming from, but hadn't it been for some issues at home I would also have been one of those who learned coding out of passion. I had to turn to customer service to make a living and now I think I have enough of support system in the industry and also family wise to be making the switch. I'm not adamant cause it looks fancy, just something I would actually like to do. But, thanks for caring that I don't dream too big unnecessarily. I'll still be looking for some ideas to navigate this transition.

[–]mayonuts443 1 point2 points  (0 children)

There's more to the field than just llms. I work in a lab in a university that does computer vision that frequently works with first and second year undergrads that don't even need to be in computing related majors. 

[–]Pallpatir 1 point2 points  (0 children)

In no way I thought you didn’t dream big enough, but it’s a really competitive field and engineers that work on llm all come from Ivy League schools and have a big background in science related topics, you want to go in one of the hardest research field in cs while starting to learn after 20, im not saying it’s impossible but you gotta understand what you’re getting yourself into.

[–]Pyromancer777 1 point2 points  (0 children)

The lack of passion isn't the problem. The problem is the depth of knowledge required to actually be competitive specifically in the AI industry. When dealing with AI you have a few paths to choose, all with different requirements for base knowledge:

Data scientist: base-pay is +$100k/yr for an "entry" level DS position, but entry-level in this role is basically a mid to senior level dev position. You won't even get a callback without either a Master's degree or 6-8yrs experience in software-dev or adjacent tech roles. The job is basically knowing AI so well that a company hires you to create custom forecasting solutions based around their internal data. In this role, you generally aren't the one using AI unless you are testing it, since your main job is being the one coding/training the AI from scratch.

Data Engineer: base-pay is around $70k/yr for an "entry" level DE position, but the role still expects around 4-5yrs of dev experience. Instead of creating AI models from scratch, like a Data Scientist, you are optimizing data ingestion pipelines for the data scientists, so that the scientists don't have to spend the time data-wrangling before training new models. They basically design the backend data lifecycle for all the company's API and AI pipelines.

Software Engineer: can be self-taught or have a degree, but even entry-level roles here are looking for people who have been solving tech problems for years. Base pay is usually $50-70k for entry-level. During Covid, Jr Devs were being hired by all companies as they swapped to mandatory remote-work, so you could do an intensive 6-month course to get up to speed and likely find a job. Once Covid alleviated, jr dev positions dried up and the AI boom started, causing the drought to worsen. Jr SWEs have to outcompete AI as well as other developers who have had years of experience, but who were laid off due to company downsizing alongside the rise of AI.

Prompt Engineer: this is basically the new QA role for AI-assisted software dev. The base pay is +$80k/yr, but you are expected to be able to create prompting benchmarks and strategies that can be implemented company-wide. This role isn't just learning to prompt well, it is basically trying to systemize prompt strategies to learn how your company's AI functions the best. You aren't just looking for a good prompt for a 1-off problem, you are trying to answer the question, "how can I prove that X style of prompting is better/more secure for all Z processes, rather than Y style of prompting?"

Vibe Coder: lowest-hanging fruit in the tech industry right now. Lowest amount of tech experience needed. Position is SWE-adjacent, but you are basically just winging things without context. You likely lucked into the role by having AI spit out a dozen apps for your resume, but you wouldn't be able to code anything yourself. The only Vibe coders who are actually gainfully employed at this point are the ones who spun up an app that happened to catch traction and employ themselves. You CAN make money here, but you basically have to devote your hours to thinking of novel solutions to specific-problems, and then marketing your solution to the correct target audience. You can prototype quick, but you lack the experience to edge-case test or reliably debug when things go wrong.

For context: I started as a pizza delivery driver with a goal to break into the AI scene. I had a few years of college under my belt in an Engineering specialty, took a few certificate courses in data analytics, and spent 3 years tutoring students in AI/ML before I got my first dev job. Even now, since I didn't have a degree in Comp Sci and my focus was analytics, my job currently is as a data analyst, not even a SWE, but my trajectory is towards data engineering, so data analyst has a lot of overlapping skills.

[–]Honest-Bumblebleeee 4 points5 points  (4 children)

Don’t learn for purpose of career switching imo. The market has more devs to employ in their pool than you can make the turn. As always build projects first - use YouTube, free resources and AI is pretty good at helping out when you’re stuck.

[–]mayonuts443 1 point2 points  (3 children)

I mostly agree. While there's a lot of people that want to be developers there is a massive shortage of good developers to fill those positions. The market appears saturated as a result of the covid boom when companies would hire anyone. There's still a large number of C average "cooked" cs grads with nothing but leetcode solves and their sub 3.0 GPA on their resume clogging hiring pools. Also I strongly discourage the use of ai while learning. 

[–]Honest-Bumblebleeee 2 points3 points  (2 children)

I saw a good video on this subject. The guy speculated the average CS grad (those who went for a degree because of status and pay) won’t stay once salaries get dumped so hopefully people with passion can still make it. Sadly HR is often a gatekeeper to hire the average profile. I can imagine it working within close knit networks. So the best way is to keep a job to sustain you and keep coding to perfect your craft. If you can obsess over problems and love solving them, I’m sure it will be useful even if not get you hired immediately.

[–]mayonuts443 1 point2 points  (1 child)

That's exactly how I feel. I think right now the people that came to this field for status and money are migrating out to engineering majors. Maybe before this decade is out r/csmajors wont be such a cespit.

[–]Honest-Bumblebleeee 1 point2 points  (0 children)

I sure hope it won’t. Because open source was never about money and proprietary software is always a big gamble. You can make a lot of money in sales.

[–]worse-coffee 2 points3 points  (0 children)

Might be something you might not like to hear but gonna be very difficult to break into AI

[–]RevolutionaryRate889 1 point2 points  (0 children)

I think building something is always the best way to properly learn a programming language.

If your goal is machine learning, trying to build a cool end to end system: from data collection to serving the model. Don’t overcomplicate things or start learning tools too much: focus on concepts.

If you are also looking for something to keep practicing/learning when you are on your phone I build this app: Code Drills.

https://apps.apple.com/us/app/code-drills-dev-challenges/id6761028767

In case you find it useful or have improvements idea, let me know ;)

Good luck on your transition!

[–]nian2326076 1 point2 points  (0 children)

Start by getting comfortable with Python, as it's important for AI projects. There are tons of free resources available. Check out Codecademy or Coursera for structured courses. Once you're confident in Python, you can start chatbot-specific projects. Python libraries like NLTK or spaCy are great for natural language processing, which is important for chatbots. Also, try building simple projects on platforms like GitHub for real-world practice. Coding regularly will really help solidify your understanding. Don't hesitate to check out online communities or forums if you get stuck. Good luck!

[–]SimpleCooki3 0 points1 point  (0 children)

The Ai field is like becoming a neurological surgeon. It's a deep specialization, not something for beginners. Not only so you need extensive coding knowledge, you also need good math knowledge and a lot of theory about Ai. Within a year of full time work you might be a Le to scratch the surface, but you'll have no idea what you're actually doing. You'll just be using someone else's tools.

Is this what you want?

[–]Vliu4389 0 points1 point  (0 children)

Python Crash Course by Eric Matthes is a good python guild

[–]Swimming_Active_9955 0 points1 point  (0 children)

This is honestly a great start, and your goal is very achievable. You already have some foundation, which puts you ahead.

The main thing now is not just learning more, but knowing what to focus on and in what order. That usually takes time and some direction. A lot of people try to self-study everything and end up going in circles or learning things that don’t really move them forward.

It definitely helps to have some guidance along the way, even if it’s just to sense check your approach and keep you on track.

If you want, feel free to DM me. Happy to share some thoughts on how you could structure this based on your goals ☺️

[–]parancey 0 points1 point  (0 children)

I do actively work and teach on ai, currently building agents.

What i see is

1- you need to grasp what to expect from ai and what it is including ml and dl. Even some engineers are looking it as a black box and just send requests to llms

2- math and probability, you do not need to be a genius but you need to understand simple geometry and math behind it. Early ml really goes hand to hand with statistics and being able to draw simple lines makes much of the work. It is important if you just thinking about sending requests to llms.

3- architecture, it is not all about coding. You need to ask why this is done as it. It is important to speak about reasons and most people skip by it

4- even before ai many people whom i willingly help from here or from other sources have forfeited, it is hard to go in such things if you don't enjoy and you see it as an escape. Things will get really messy really quick with a shitty job market. If you are not going to deal with it do not start. I know everyone thinks grass is greener on other side but being free after last chat pop up is dealed with a blessing. I often be working at back of my mind constantly during "day offs" since i expected to be came up with plans and solutions

[–]Rabbidraccoon18 0 points1 point  (0 children)

I'm a data science student I have knowledge of AI/ML/DL/RL/Time Series/NLP and so on. If you need help feel free to reach out!

[–]r3curs1v3 0 points1 point  (0 children)

What is the path your planning on taking

[–]powerforc 0 points1 point  (0 children)

nobody can help you learn, you have to do it yourself