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.
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”
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.
I think that one has to be a data scientist first in order to be an ai engineer. The reason is that you can’t engineer something that you don’t understand like a data scientist from the ground up. That being said, data scientists wear many hats and the ai engineering role can be included. I think the only difference seems to be that the engineering side demands more complex data and doesn’t do a lot of structured data analysis.
I am also a data scientist. I have been using LLMs to help with my feature engineering. What was mostly useless free form text fields can now easily be cleaned and standardized. One example I am working on takes a duration, which can be anything from “one week” to “oh idk maybe half a month” and converts it to integer day value. The LLM is able to transform into a 7 and 15 respectively and note whether it’s an estimate based on ranges or language. Pretty cool stuff.
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.
I see the AI Engineer (at least in the context explained above) as a SWE that builds AI applications. Not that there’s anything wrong with that. When I think of AI Engineer, I typically think of someone actually building the LLM.
Thats true. If we compared the roles: The AI engineer, deploys the model, prebuilded as service (SaaS) form GCP, AWS, Azure, etc from the requirements from the R&D team. On the other hand, the Data Scientist (MLEng) role is focused in build the intern arquitecture to build the algorithms or make finetunning to the models using diferents methods from the bussiness requirements (Data Analyst). If youre in a Hybrid Role, maybe youre Data Scientist focused on building AI products, or Machine Learning Engineer with Bussiness background or something similar, but the Science is not equal to the Engineering. The perpspective is different. you´re right.
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, 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.
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, 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
1. generative AI, or big new models that use multiple stuff to classify or make regression. Also, robotics. 2. A LOT, learn math and statistics, the rest doesn't matter 3. Internet. Start with datascience, math and statistics. Within datascience you need to learn about common models (MLP, SVM, etc). After that, start understanding LSTMs, CNNs, dropout and batch normalization. In the end, after around 1 or 2 years, start learning transformers, visual transformers, and also diffusion generative models. Start with any calculus and basic math videos, also basic statistics. After that, use a course from udemy and youtube that talks about sklearn. And then go through computer vision with deeplearning and time series prediction algorithms... it is a possible way.
@@vitorpmh Thanks for the response. 1. I know about these GAI. Any other type of sub-areas/fields based on different criteria. 2. Any fields/areas that requires less math. I heard, interoperability is one areas where no/less math. But not sure if it can be considered AI engineer. Also, prompt engineering. Any thing else? 3. I just finished Google AI essentials from Coursera. I'm coming from Social science background but has STEM background as well. So, expecting some AI related skillsets (but not hardcore) and I also have Biology/life-science related domain knowledge. Any suggestions?
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.
He isn’t writing backwards: the video has been mirrored; the same goes for all the videos I have seen on this channel. To verify, confirm that they all appear to be left-handed, which is very unlikely.
@ThoughtfulAl - I'm right with ya... I had to buy brain supplements.... last time I learned anything even close to technology was Lotus 1-2-3 & WordPerfect. That got me my 1st real job. BUT with so much help & support here on TH-cam I believe we shall succeed!!
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
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 :)
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
@@babasathyanarayanathota8564what’s required is that you must know ML and when to use it as “AI engr” is an applied field. Data science isn’t required but is a plus.
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.
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.
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.
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!
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.
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.
Data scientists may use AI techniques in their analysis, while AI engineers specifically focus on the engineering aspects of AI model deployment and system integration. Therefore, AI engineers can be considered a subset of the broader data science field, with a specialization in artificial intelligence technologies.
honestly, all i see is conflits. i think i disagree with this definitions. the definition of a data scientist here is actually pointing to data analytics. I think data analytics, AI Enginerring, ML enginerring and so on are all subset of data science. I think its a matter of area of specialization.
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 ?
The 'Prescriptive' capability or specifically Prescriptive Analytics has always been part of Data Science. I found in this video, it switched side. And as you mentioned AI is more seen from Application side, specifically GenAI for its ability as productivity booster.
I’m a junior Computer Science student considering a career in Data Analytics or Data Engineering. I have experience researching business operations and providing insights for improvement, backed by three years of active stock trading. My critical thinking skills have guided my investment decisions, focusing on companies that align with my insights. Initially, I aimed for a career in software engineering, but the rise of AI and increased competition have shifted my focus. I have strong Python skills and plan to learn SQL and advance my Excel knowledge and learn other things that are truly needed for this field.
No, data scientists don't always use AI, as mentioned at the beginning of the video. But it is very common. And prescription is a task of data scientists.
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.
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.
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
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...
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 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?
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.
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 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.
Fine tuning is usually referred to in connection to nural networks when one takes a base model of some sort and continues training the model on a specific domain of the problem at hand
I don't fully agree with the definition of AI Engineer; you reduced the role to 'just' working with GenAI. AI as term is much broader than that and engineering is not science. You could apply the role of an "AI Engineer" to any activity you do that involves building AI components; the size of the data or model doesn't make much difference here. Building an AI system using a shallow neural network (e.g. MLP) is also AI Engineering. Approaching the science behind it from a data perspective c.q. data-driven approach is Data Science. On the other hand, working on LLM models is also "AI Engineering" and doing the statistics and explanations is "Data Science". These two go hand-in-hand.
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 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😮
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.
very clear, thanks for this gonna help me so much rn.
This video explanation is so perfect , from the non distracting background to the drawings / key points , to the voice and tone . Thankyou ^_^
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”
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.
I think that one has to be a data scientist first in order to be an ai engineer. The reason is that you can’t engineer something that you don’t understand like a data scientist from the ground up. That being said, data scientists wear many hats and the ai engineering role can be included. I think the only difference seems to be that the engineering side demands more complex data and doesn’t do a lot of structured data analysis.
I am also a data scientist. I have been using LLMs to help with my feature engineering. What was mostly useless free form text fields can now easily be cleaned and standardized. One example I am working on takes a duration, which can be anything from “one week” to “oh idk maybe half a month” and converts it to integer day value. The LLM is able to transform into a 7 and 15 respectively and note whether it’s an estimate based on ranges or language. Pretty cool stuff.
I agree, I used to mostly create models based on unstructured data when I was working in text analytics.
AI LLMS can be data scientist itself
Chill NOTHING in life can be 100% one thing, there always be a threshold of options and other ways to do anything.
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.
I see the AI Engineer (at least in the context explained above) as a SWE that builds AI applications. Not that there’s anything wrong with that. When I think of AI Engineer, I typically think of someone actually building the LLM.
Thats true. If we compared the roles: The AI engineer, deploys the model, prebuilded as service (SaaS) form GCP, AWS, Azure, etc from the requirements from the R&D team. On the other hand, the Data Scientist (MLEng) role is focused in build the intern arquitecture to build the algorithms or make finetunning to the models using diferents methods from the bussiness requirements (Data Analyst).
If youre in a Hybrid Role, maybe youre Data Scientist focused on building AI products, or Machine Learning Engineer with Bussiness background or something similar, but the Science is not equal to the Engineering. The perpspective is different. you´re right.
Chill NOTHING in life can be 100% one thing, there always be a threshold of options and other ways to do anything.
You know what IBM. YOUR COMPANY WAS DREAM COMPANY. WITH HELP OF THE SHORT CONTENT WHICH EASED ME LANDED IN FRESHER DEVOPS JOB . THANKS
Congrats!
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, 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.
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,
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
1. generative AI, or big new models that use multiple stuff to classify or make regression. Also, robotics.
2. A LOT, learn math and statistics, the rest doesn't matter
3. Internet. Start with datascience, math and statistics. Within datascience you need to learn about common models (MLP, SVM, etc). After that, start understanding LSTMs, CNNs, dropout and batch normalization. In the end, after around 1 or 2 years, start learning transformers, visual transformers, and also diffusion generative models.
Start with any calculus and basic math videos, also basic statistics. After that, use a course from udemy and youtube that talks about sklearn. And then go through computer vision with deeplearning and time series prediction algorithms... it is a possible way.
@@vitorpmh Thanks for the response.
1. I know about these GAI. Any other type of sub-areas/fields based on different criteria.
2. Any fields/areas that requires less math. I heard, interoperability is one areas where no/less math. But not sure if it can be considered AI engineer. Also, prompt engineering. Any thing else?
3. I just finished Google AI essentials from Coursera. I'm coming from Social science background but has STEM background as well. So, expecting some AI related skillsets (but not hardcore) and I also have Biology/life-science related domain knowledge. Any suggestions?
So beautifully put together! Thanks for shining some light into this AI thing. It's much easier to understand the whole picture 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... 😅
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.
He isn’t writing backwards: the video has been mirrored; the same goes for all the videos I have seen on this channel.
To verify, confirm that they all appear to be left-handed, which is very unlikely.
@@solsospecialyeah I always came to this same conclusion.
You looking for sympathy old-man lol ! it's like "great oldman go for it" all people should be like you !
@ThoughtfulAl - I'm right with ya... I had to buy brain supplements.... last time I learned anything even close to technology was Lotus 1-2-3 & WordPerfect. That got me my 1st real job.
BUT with so much help & support here on TH-cam I believe we shall succeed!!
literally the perfect video for me right now
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
Great presentation. Super clear. I can’t wait to watch more of your talks. Thanks
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 :)
could i kindly ask in what should i focus on to be good ai engineer ?
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.
Thank you so much for the clarity!.. What a Wonderful video!
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
Hi , is data scientistit requirement to become ai engineer . I am from devops
@@babasathyanarayanathota8564 AI engineer in this context has no meaning, what they say in the video it’s wrong
@@babasathyanarayanathota8564you need data experience to get a job as a mle
@@babasathyanarayanathota8564what’s required is that you must know ML and when to use it as “AI engr” is an applied field. Data science isn’t required but is a plus.
I am very disappointed in this video as well.
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.
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.
Dude, GPT 4o can’t even generate simple code correctly without mistakes. Your job is safe.
@@superuser8636You must not be using it correctly then. If you prompt it correctly then it can get great results on many different types of tasks
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.
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!
Keep these vids coming!! 🔥🔥🔥
You are a great teacher. I love your analysis: top-notch
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.
Best explanation on the topic
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.
Data scientists may use AI techniques in their analysis, while AI engineers specifically focus on the engineering aspects of AI model deployment and system integration. Therefore, AI engineers can be considered a subset of the broader data science field, with a specialization in artificial intelligence technologies.
Awesome, What a beautiful explanation, part by part. Thank you
honestly, all i see is conflits. i think i disagree with this definitions. the definition of a data scientist here is actually pointing to data analytics. I think data analytics, AI Enginerring, ML enginerring and so on are all subset of data science. I think its a matter of area of specialization.
This guy wrote everything and drew everything in reverse. What an effort
These screens can be flipped…
Pretty interesting.
I'm gonna start learning Data Analysis.
Very helpful info.
enjoyed video wondering how you do annotation of your notes
2017 to 2022 Organic Ai and 2023 + Gen AI
Great presentation
wow great breakdown, thanks professor Isaac, I learned a lot 🤔
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 ?
The 'Prescriptive' capability or specifically Prescriptive Analytics has always been part of Data Science. I found in this video, it switched side. And as you mentioned AI is more seen from Application side, specifically GenAI for its ability as productivity booster.
I’m a junior Computer Science student considering a career in Data Analytics or Data Engineering. I have experience researching business operations and providing insights for improvement, backed by three years of active stock trading. My critical thinking skills have guided my investment decisions, focusing on companies that align with my insights.
Initially, I aimed for a career in software engineering, but the rise of AI and increased competition have shifted my focus. I have strong Python skills and plan to learn SQL and advance my Excel knowledge and learn other things that are truly needed for this field.
No, data scientists don't always use AI, as mentioned at the beginning of the video. But it is very common. And prescription is a task of data scientists.
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.
with GPT store in place . do we really need to work on foundation model to get the result we want?
What are the differences between a ML engineer , AI engineer and Datascientist
Issac Ke my GOAT!!!!!
Well explained! THANK YOU.
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.
Hi Issac! great job explaining the difference between data science and AI engineering! I really enjoyed your video!
Thanks for Explanation ♥️♥️♥️
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
Always Data Science 🔥💥👇
Thank you ♥
"AI engineers" are just software engineers who dabble with OpenAI API calls.
Not even close.
Consider engineers who are prepare OpenAI APIs.
@@mentalmapper+1
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...
Great! Thank you.
Thank you for sharing this
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.
Please we need AI engineering roadmap 🙏🙏🙏
Very nicely explained
I'm A.I engineer...!!! Amen...!!!
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?
Cheers!
Thanks
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.
I really don't think the details of this are right. Plenty of data scientists work in generative AI / LLMs / unstructured data.
Yes, but this means they are transforming into AI Engineers
How is able write on the screen???
Whoop yessir Isaac
I really like the expression "data storyteller"
Either my brain is broken, or you’re writing backwards on glass 🥴😵💫 if so, that’s a crazy talent
It was more of a data scientist vs generative AI engineer
great video
I am looking for a colleague to advance in the field of data science, someone I can exchange ideas with and learn together
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 study data science... the AI Enginering seems need more people to work.... data science can be done by one SCIENTIST...?
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.
Where does fine tuning fit in all these ?
Fine tuning is usually referred to in connection to nural networks when one takes a base model of some sort and continues training the model on a specific domain of the problem at hand
PEFT
They just changed your title dude, it’s the same thing
lmao
Anything is possible?
Awesome 👍
excellent
I don't fully agree with the definition of AI Engineer; you reduced the role to 'just' working with GenAI. AI as term is much broader than that and engineering is not science. You could apply the role of an "AI Engineer" to any activity you do that involves building AI components; the size of the data or model doesn't make much difference here. Building an AI system using a shallow neural network (e.g. MLP) is also AI Engineering. Approaching the science behind it from a data perspective c.q. data-driven approach is Data Science. On the other hand, working on LLM models is also "AI Engineering" and doing the statistics and explanations is "Data Science". These two go hand-in-hand.
I am a Chief Generative AI DataDevSecFinMLOps Cybersecurity Architect Scientist Engineer Officer 😅
Also a saiyan
Umm, i think AI Eng is fullstack eng that knows about llm api
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 one handsome fella😍
Only a data scientist would think that it's the sexiest job 😆. That being said, great video
This is more like Data Scientist vs Generative AI Engineer
I'm still an undergraduate, any tips to land on a big comapany(Google, IBM, etc.) as an AI engineer.
Good question 🤣 A good internship a good knowledge of artificial intelligence and good projects on the portfolio
AI Eng. -> applied level
Great
Confusion..🙄
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😮
Interesting
Each company has a different set of responsibilities/expectations for each of these roles, there is no standard scope/definition .. relax people 😂
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 🤫
Hi cus!
So many opinionated and false statements in one video 🤦🏻♂️
Wouldn't expect this from an official video from IBM
agreed would have been better from fly on the wall not fly that moved walls
"prompt engineering" lol.. do you use chatGPT to help you come out with an "engineered prompt" ? The new form of engineer, prompt engineer!
I stopped watching at "DATA STORYTELLER" 😆😆😆
We know why IBMs are relic now. It’s this hyper corporized approach. At Apple we call em all data scientists and they do everything
AI Engineering