Data Scientist vs. AI Engineer

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.

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.

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.

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.

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.

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

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

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

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.

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

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... 😅

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.

Keep these vids coming!! 🔥🔥🔥

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

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!

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

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.

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

enjoyed video wondering how you do annotation of your notes

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.