Data Scientist vs. AI Engineer

Breakthroughs in generative AI have given rise to the growth of an emerging AI Engineering role that is differentiating itself from traditional data science. Do these two disciplines focus on the same problems? Is there any overlap in techniques and models? In this video, Isaac Ke, a former data scientist turned AI engineer, explains key differences and similarities between the two fields, along with some of the emerging trends gripping the AI landscape.

[munchingmogul]

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

[bayesian]

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

[dusanbosnjakovic]

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.

[cuddy]

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

[patfov]

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.

[petrusdimase]

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.

[jonathanreef]

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... 😅

[DillonLui-xyex]

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

[selvakumars]

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

[panchao]

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.

[stt.]

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.

[NaijaStreets-mrbl]

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

[mikeeotool]

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.

[vidivy]

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

[franciscomedinav]

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

[saidshikhizada]

enjoyed video wondering how you do annotation of your notes

[OxidoPEZON]

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!

[anythinggoes]

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.

[ONEOFONE]

literally the perfect video for me right now

[babasathyanarayanathota]

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