This video explanation is so perfect , from the non distracting background to the drawings / key points , to the voice and tone . Thankyou ^_^

The DS scope is only EDA, feature engineering, giving business insight and story telling. More than that is area of MLE and AIE. Data Science is generating insight from "data". Building the statistical analysis, gain thr business efficiency or profit. Mostly use SQL, Python, Sklearn. Working with Jupyter notebook. ML Engineer is developing, serving, maintain the ML model. Sklearn basis. Pytorch. Tensorflow. NLTK. May use Python, C, Java, C# etc. Working with Postman, MlOps. AI Engineer is Implementor or Enabler of AI solution that may combine either pretrained ML or AI or Gen AI. AI may be processing of language, image, audio, artificial voice, ocr. May use Python, Java, C#. Working with Docker, Linux server. It all clear.

Hmmm. Im a data scientist and there seems to be some concepts that I find wrong or misleading. 1) data scientists can also do prescriptive tasks aside from prediction and classification tasks. In fact the last project that I worked on was in the prescriptive analysis domain 2) data scientists also deal with texts and media data. From my experience that largest I handled so far is around millions of these data 3) data scientists are not limited to traditional ML models and Neural Networks. In fact, pretrained models are also used to speed up the training process with some fine tuning involved.

Thank you for the explanation. But I feel they are not even on the same level. To me AI Engineer is a subtype of MLE who focus ML application which uses LLM. I would compare between DS vs MLE. And to me the comparison boils down to compare science vs engineering. Each has a totally different mindset when tackling the same problem. While engineer approach a problem from a system perspective, scientist approach a problem from an inference perspective.

Great effort. I think it's a discussion that we should be having over the next few years. But it's definitely premature. Just like data science became a field long after people were actually practicing data science, we will only realize the differences a bit in retrospect.

Hi, Thank you for such a huge clarification. However, can you please shed some more light on these regarding AI Engineering: 1. What are the sub-fields/areas under AI Engineering? 2. How much math is required to become AI Engineer? 3. Where can I learn the fundamentals/essentials to become an Applied AI engineer? TIA

You know what IBM. YOUR COMPANY WAS DREAM COMPANY. WITH HELP OF THE SHORT CONTENT WHICH EASED ME LANDED IN FRESHER DEVOPS JOB . THANKS

Thank you, I build RAG applications as an intern and never really knew how to qualify my job. I do some data science like scraping and cleaning data but I also do prompt engineering among other things. I don't train the models per say though or even fine tune them (for now), so was reluctant to say I'm an AI engineer but given your description I guess it's coherent.

I am learning AI, but it is pretty slow for me as I am an old truck driver although I did computer repair and builds for 12 years. My wife is a clever engineer like you and she can also write backwards fluently like you did here, but in real-time (not post-production). She is also learning AI now.

Really well explained and summarized! 😊 I am currently working on my bachelor's thesis and can absolutely confirm that I am currently using (almost) all techniques from both sides. The overlap in my area/subject is extremely large and quite often I have to be very creative when it comes to obtaining and processing information... so definitely both sides... 😅

GREAT INFORMATION ! to improve your presentations you could pre-layout all the topics and then animate each explanation point with the audio track, this would allow you to display a more detailed graphics with a huge visual impact, all of this will translate in more subscribers.

Great presentation. Super clear. I can’t wait to watch more of your talks. Thanks

Basically, every five years a new career is dropped. When that happens, all the other new drops from the past 15 yr like data science, business intelligence, ML engineer, start to look more like SQL queries. In 2034, we’re all going to say we’re “sentient robot engineers”

So basically I switched from Data Scientist to Ai Engineer without even knowing. I’m a bit surprised to hear this from IBM … it sounds a bit wrong, I didn’t know IBM competence on AI has dropped this much

Thank you so much for the video! I'm learning Gen AI so it really helped me understand the differences between data scientists and AI engineers.

It's great and funny: Im building an Agent Workflow right now that is basically intended to do feature engineering, or to put it in simpler terms: do data cleaning all alone to then work with the data. as i didn't come from classic data science i had no clues on the wording but this great video definitely helps and creates confidence in what i'm cooking right now :D

Thank you so much for the clarity!.. What a Wonderful video!

You are a great teacher. I love your analysis: top-notch

I think we are still missing another role, it's the software engineer who tries to integrate AI in legacy or in a existing enterprise appliacation landscape. He uses java/spring for example on a daily basis and tries to level up existing proceses inside his applicaton with AI.

literally the perfect video for me right now

"AI engineers" are just software engineers who dabble with OpenAI API calls.

Keep these vids coming!! 🔥🔥🔥

wow great breakdown, thanks professor Isaac, I learned a lot 🤔

From scientist to engineer to technician. Since I mostly use NLP I'm excited of the possibilities of llms but fear the models will become so good that we will shortly simply have to take the back seat.

Pretty interesting. I'm gonna start learning Data Analysis. Very helpful info.

Awesome, What a beautiful explanation, part by part. Thank you

The AI Engineer part only talks about LLMs (ChatGPT,Gemini types of models) only, which feels heavily misleading. Reducing the whole field of AI just to something that has been popular for the last few years is not really understandable. Also I think using the term Generative AI for LLMs is another misleading thing. We can also generate videos, audio, images, 3D structures with AI. Back in the day when image generation was popular people used to use generative term for images. Another problem in the video is that we don't always use "Foundation models". The video shows as if AI Engineers mostly finetune (adapt) these foundation models. Don't let this video think that AI is just finetuning LLMs. We have lots and lots of stuff to do in the field of AI :)

As you are an example of DS pivoted to AIE, how would you transition from one role to another? I am really interested in what you describe as AIE, but recently landed a job in DS, so I was curious what steps could I follow in the long term to shift my carrer to what I really want to do. Thank you!

ML engineers are data scientists that develop scalable ML pipelines and bring research to production following MLOps standards (they work together with data scientists) and know the math and SE. Being a ML engineer includes being able to deploy models as microservices that get consumed by multiple “AI” applications. One thing is the model and another are applications that consume the models and apply certain business logic In my opinion the new “AI engineer” is a very misleading term for backend software engineer that knows how to connect/use to AI apis

Hi Issac! great job explaining the difference between data science and AI engineering! I really enjoyed your video!

Great presentation

Best explanation on the topic

enjoyed video wondering how you do annotation of your notes

I once trained a Transformer Neuronal Network for a Task called "image registration". The network recieves to images and outputs one image. Training time was one week. Still not sure if this was a Data Science Project or a AI engeneering project.

Im so surprised that this video felt like an oversell of AI engineer and GenAI stuff. Most of the usecases he compared are wrong. DS side is almost like a process if all ML applications while AI eng side just appliactions. Also where is evaluation? Explain ability ?

They just changed your title dude, it’s the same thing

Thank you for sharing this

This video is informative. However, I feel Prescriptive capability or 'Prescriptive' analytics has always been part of Data Science. I have seen Data Scientists with exceptional domain knowledge, building Prescriptive Analytics systems. However, in this video, I was surprised to see, how 'Prescriptive' analytics switched sides - as it too got heavily influenced in the newfound AI (or GenAI) rage. On the other hand, I feel - AI is more towards Applications, and specially GenAI with a promise of productivity booster.

Well explained! THANK YOU.

Please we need AI engineering roadmap 🙏🙏🙏

Very nicely explained

with GPT store in place . do we really need to work on foundation model to get the result we want?

2017 to 2022 Organic Ai and 2023 + Gen AI

Data scientists train the models and ai engineers choose the required models for each step of whatever ai tool they are building.. is that a good analogy? Fin analyst vs portfolio manager relation? Btw didn’t mention langchain Also with dspy, prompt engineering shld be dead. Dspy adds reasoning to prompts

Thank you ♥

I really don't think the details of this are right. Plenty of data scientists work in generative AI / LLMs / unstructured data.

Great! Thank you.

Thanks

Either my brain is broken, or you’re writing backwards on glass 🥴😵‍💫 if so, that’s a crazy talent

Thanks for Explanation ♥️♥️♥️

I must disagree. I just finished an MSc in AI, and we learned everything you mentioned in the Data Science section and the AIe section, but nothing you mentioned, we learnt math behind the algorithms, etc.

What are the differences between a ML engineer , AI engineer and Datascientist

great video

I think this is a wrong description of AI engineer. What he was describing is mostly LLM-focused SWE integration engineering. While that has its place and AI engineers would do that, it’s a small scope and it’s akin to web backend developers vs actual backend hardcore engineers (with memory/parallelization management, GPU utilization and such). Good start for a generalist SWE who wants to deepen their scope BUT ITS NOW AI ENGINEERING.

Cheers!

I’m interested in going into the field of data science/ machine learning. I’m currently a self taught programmer who have done text book math in years. Can anyone in this profession tell me what the math requirements are for these roles?

Issac Ke my GOAT!!!!!

I like the comparison but I do note that Data Science was not presented fairly, he could've said that Data Scientists lately do work on million of rows data, using Deep Learning algorithms, just a side note*. But thanks for the video! Great job.

I'm A.I engineer...!!! Amen...!!!

Thanks, but in my point of view it's a very strange definition for AI Engineer... Fondation Model, RAG, LLM... are just a little part of AI system and AI engineering needs... For me, when a DataScientist build a global system to make real-time prediction with industrial sensors, for example, he is an AI Engineer...

How is able write on the screen???

It was more of a data scientist vs generative AI engineer

Always Data Science 🔥💥👇

I study data science... the AI Enginering seems need more people to work.... data science can be done by one SCIENTIST...?

AI is much more than "Generative AI"... Just take into account the term "AI" itself didn't suddenly appear when Generative AI took off a few years ago, it is much older and much more encompassing than that.... There's even mention about how "Generative AI has split off from AI into it own distinct field" at the beginning of his video. Many in the comments section have mentioned valid criticisms to what's exposed in the video, so i'll just limit myself to saying the name "AI Engineer" already seems incorrect for the concepts exposed, and in this case seems to be used only because of the current hype in GAI.

Where does fine tuning fit in all these ?

Awesome 👍

I appreciate this distinction. There are nuances but the inputs are different, tuning techniques and evaluation approaches are different. This view is opinionated and could offend a Data Scientist who knows neural networks very well (and can create foundation models rather than just use it). But you could have someone on the right who cant do the ones on the left. And someone on the left who despite knowing a lot needs to become familiar with techniques on the right. They can cross but given that additional work is needed, its reasonable to say they are different. There is enough work that I think we need the distinction and if you can do both then yey for you. Maybe it should be GenAI/TransformerAI Enginner rather than just AI engineer but we can keep it simple.

I am looking for a colleague to advance in the field of data science, someone I can exchange ideas with and learn together

excellent

Anything is possible?

Whoop yessir Isaac

I'm still an undergraduate, any tips to land on a big comapany(Google, IBM, etc.) as an AI engineer.

This does not sound right. Sorry, IBM. Relating AI Engineer to Gen AI (2 years old field) is obviously wrong. If this is the case, then 90% of today's Data Scientists are also AI engineers, and this distinction does not make sense anymore😮

This is one handsome fella😍

Interesting

Writing backwards is an AI Engineer-type flex

Great

Umm, i think AI Eng is fullstack eng that knows about llm api

Explained with good concepts, but... Data Science: name of a career fundamented on statistics and computer science that already existed and has had updates over the years. While AI Engineer is the name of a vacant position. A data scientist is capable of doing everything you describe on the right side of the board and beyond, why? knows Statistics and data, and the fact that it is not structured is still data. You are comparing the man who knows how to build a car with the man who drives it.

I am not sure if people know what is our capability they would let us use it because it is just like omni potent and omnipresent which is God like and we can even decide what individual fate is to be or not to be which may not be for humam biased

This is more like Data Scientist vs Generative AI Engineer

I am a Chief Generative AI DataDevSecFinMLOps Cybersecurity Architect Scientist Engineer Officer 😅

Cannot call someone as AI engineer if they are just using others models.

Confusion..🙄

Hi cus!

We just get our ai guy to do both jobs (and sort our website out all the time), no one show him this video or else he might ask for more money 🤫

So many opinionated and false statements in one video 🤦🏻‍♂️ Wouldn't expect this from an official video from IBM

"prompt engineering" lol.. do you use chatGPT to help you come out with an "engineered prompt" ? The new form of engineer, prompt engineer!

AI Eng. -> applied level

avoid any title using Data ..prefer to be Software Engineers ...you dont want to be begging for compute just to productize your models....

I stopped watching at "DATA STORYTELLER" 😆😆😆

Cute guy. 😍

AI Engineering

I think it is more Generative AI Engineer ..

AI engineers will survive.

5- AI, in order to improve its performance and prevent undesirable consequences, must continuously interact with “effective rules and stable principles in the realm of existence”. @jamshidi_rahim