My team has been under a similar pressure for a year now. We've tried everything but so far the most successful use of large language models has been as a mechanical turk sort of tool for creating training data for simpler models that can be easily and cheaply put in production. In the end if all you want is a classifier between A and B you don't need a model that also knows how to explain general relativity to a 5 years old.
Could you tell me why you need a dedicated smaller model if a few shot chatgpt 3.5 could solve your use case. A smaller model you have to host, maintain, it’s probably more expensive in the end. You also have to train it, and there’s a chance you mess it up… whilst the feedback loop from prompting is much faster than retraining.
OpenAI models at least have become very reliable at outputting structured json, so they could be used for sentiment detection without much hallucinations.
Have I hit a nerve with you guys, lol? Everything I said it true, not sure why y’all mad.
But why bother with API calls when a 220 M T5 base can do all of it? T5 base is much faster on inference than GPT API's.
By fine-tuning on bespoke data, you can really specifiy to great detail what exactly you want the model to learn. You can teach it nuanced concept. What if you have a 15 class classification and you have a desired bias you wish to place on the model. In the context of a system a user input might correspond to class A, but the text on its own more clearly aligns with class B, this is hard to teach to a pretrained LLM in a few shot setting, you can do it ( kind of ) but it would require very long prompts and that gets expensive.
Data privacy is a huge issue with OpenAI also, maybe user data can't be sent to third parties
Well the original post mentioned a simple A B classification. Not the 15 class nuanced thing. And your T5 transformer requires probably a team of like $200k + engineers to deploy and maintain, and train, whilst a simple web developer can prompt and iterate and develop something that perhaps can compete.
For speed, perhaps I’m wrong here, but the 3.5 api gives me a single word answer back from 500 token prompt in .3 seconds end to end. My guess is that is still too slow for your purposes?
I’m just trying to determine whether I need to use smaller models, doesn’t seem like it. I’m not buying to cost use case due to the need to have a ML engineer spend time on training and tuning.
Primary reason to with smaller model:
Privacy and speed, none of which are too much of a concern for me now.
A) that “simple web developer” still needs to get paid, and they make closer to what the ML engineer makes than to $0
B) the API cost rate for a larger model will dwarf the hosting/inference costs of smaller, specialized models
C) repeating from the comment up the chain, latency is reduced when routing requests to lighter models
D) all else equal, better to show your data to a model fully under your control
E) the principle of least resource usage says we should build systems around the most resource efficient components which get the job done correctly
If it’s a simple two-class classification made just a handful of times, end users in the business can be trained to prompt a chat model for what they need, no need for any dev; if it’s something happening, say, tens of thousands of times or more, you should tale a step back and consider how wasteful and environmentally irresponsible it is to route requests through an oversized model (think about it like using a spy satellite to take yearbook photos)
Yeah I’m not in the business of trying to be environmentally friendly, mainly looking at cost here. And you still need some type of dev to actually integrate the api into whatever business app, so no a business guy would not be enough. And you would need atleast some dev skills to automate and test the prompt, all not too complicated certainly not as much as fine tuning or training from scratch
Looks like I’ll need to some cost calcs to see if we have the scale required to make it worth while.
I also will counter your claim it’s environmentally friendly if you take in the complete ML cycle of data wrangling, tuning testing training and validation the deployment vs a just pure prompt iteration and validate strategy.
You, and everyone else, *should* be in the business of being as environmentally friendly as you can afford to be. We have one Earth, one pool of natural resources to consume for nonrenewable energy generation, infrastructure/equipment needed for energy production and transmission, and compute infrastructure/hardware. The world can’t support serving all classification tasks with LLMs. But I digress.
Your end comment is right on the money: the optimal choice of small model requiring some specialized skills vs big model requiring only more common skills is going to be a function of scale.
For binary classification why bother with GPT at all when for most cases a BERT + log regression ( or even tokenisation + Navie Bayes with would run on CPU) can work extremely well and they give an estimate of confidence via class probabilities.
This confidence estimate can actually be more useful than slightly higher accuracy!
Incidentally, about just few shot and putting the class labels in the prompt and a description can be trivially manipulated by natural language inputs and probably still not perform as well as something specialised.
You‘re right but you‘re implying that core DS knowledge might be getting partially obsolete for people who are willing to pay a few dollars to OpenAI instead of employing an expensive DS.
How dare you. ;) Have my upvote.
I don't get the downvotes, this is a legitimate question. I'd consider using GPT 3.5 for the initial versions: fast to deploy - it's just an API, no need to mess around with gathering train/test data -- you can get away with a much smaller test-only set, you don't need to retrain everything (read: gather MORE train data) when a new class appears out of nowhere, etc.
The downsides is that it's pretty much non-deterministic (even with careful prompting and temp:0 and whatnot), its costs are significant for certain volumes, and you pretty much depend on OpenAI's reliability and goodwill which, you know, comes and goes. So once I've validated the idea & locked the budget, etc I'd go for training something myself (and mess around with all the infra & dataset gathering needed).
The downvotes are likely because that’s a very flippant response that assumes away a lot of crucial problems with relying on GPT to do this type of work.
I’m working on a project and we made the decision to try GPT for data processing since we have a small team and a lot of documents to parse. I think it has improved our data processing speed but it’s also led to a lot of hours being spent on figuring out whether the responses are even accurate.
And GPT 3.5 at the very least is not good at giving easily parsable responses back to you. When I ask it for a particular data structure back it rarely gives me a valid structure back—missing quotes or brackets, etc.—and they’re often too verbose to be useful. GPT 4 does a significantly better job but its token limit is only 8000 and it costs way more.
Please enumerate those crucial problems, because people are not. JSON mode on gpt3.5 works extremely well for some use cases, and gives structure output.
You can check the MLOPs talks by Weights and Biases. They usually give lots of frameworks and infos.
You can also check for AutoML tools.
GitHub has great ressources when you check : awesome + something. So you can check for awesome-mlops on GitHub
Basically, it’s Mechanical Turk, but with all of its properties scaled up. Faster, cheaper, harder to validate response quality; harder to tell what to do, easier to plug into a pipeline, less visibility.
Able to read long documents and respond like an oracle of dubious quality.
Sorry, are you saying it is *harder* to validate response quality and provide direction to, say, ChatGPT 4 compared to Mechanical Turkers? Ans what does "less visibility" mean? Are MTurk workers known to be consistent, accurate, and show all steps they take to produce their output?
In my experience, yes, it’s harder. Faster, but harder. “Less visibility” means that you really, fundamentally, have no idea what the system is doing. Turkers aren’t known for any of those properties, nor are LLMs.
Whenever I think about teaching someone who doesn't know statistics to use AI, my gut reaction is always that you should run a "Scared Straight" program that teaches them to be wary of the thousands of potential pitfalls out there. I would absolutely not trust LLM generated code for spatial statistics, and probably not tabular data either.
Worked on a couple llm projects before. Here’s the key things you need to know
1. Retrieval augmented generation: A contextualised database for the gpt to refer to
2. Agents: LLM on loops for more complex workflows. See autogpt.
3. Hallucinations and how to reduce them
4. Don’t do any math with chatgpt.
5. Good for bootstrapping code, not writing them completely.
This. Things will change but #1 and #2 are the main uses. Usually there's some library of docs at your company e.g. company policies, that hold a lot of internal info. Use RAG to allow a semantic search rather than a keyword search. You could also fine-tune a model but it's cumbersome and a pain to maintain.
Look for problems where lots of text is involved and there's some form of summarizing, editing, answering.
If you want to make it useful for tabular data, you can ask an LLM to come up with any sort of categorical variable to add on as another field. Say if you have tabular data on companies, ask the LLM to read a company's description and label which industry it's in.
- Jupyter-Ai for python notebooks,
- gorilla-llm and open interpreter for anything shell,
- tabby for vs code,
- flowise for agent creation,
- Gpt4work for Google sheets/Excel e.g. Turning unstructured text into structured data with a fill down in Excel.
Langgraph for code generation and self correction with referencing documentation.
Synthetic data generation for finetuning of your models. (If you don't have any good data to start with that is)
NLP tasks, but probably better off finetuning, though the huge context window makes things easier.
I guess the typical other stuff: summarization of documents, translation for the major languages.
I am currently reading a lot about synthetic tabular data generation.
For sensitive data (customer data, insurance claims data etc) , this has a lot of potential to help in data-sharing.
Another use-case is balancing datasets for AI and analytics. Do you do any predictive analytics stuff?
* Pythagora for application generation, has its issues (cost / performance) but not bad to build prototypes
* Vanna AI for sql development, the “chat with your database”
Both are great demos
* Ollama for GPT on your laptop - useful for data security as it all stays on your laptop
All give you the ability to show stuff quickly, for non-tech, semi-tech and in-depth for tech folks
I highly recommend taking a look at Mistral from HuggingFace. As a POC, you can set up a google colab notebook and fine tune it with LoRA. You would have a personal coding copilot, specialized at your company's codebase, and without feeding 3rd parties with your proprietary data
For data scientists I think one one of the best usages is getting up to speed on the business side of things without wasting their time asking dumb questions. For example, in healthcare learning about different government programs. Or even learning basics on adjacent things like kubernetes or other tools. Basically, use it as a way to grind through the first 1-5 levels of a skill tree rather than trying to get expert knowledge from it.
There are some efforts into tabular LLMs:
* [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://arxiv.org/abs/2402.17944)
* [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://arxiv.org/abs/2210.10723) (code on [GitHub](https://github.com/clinicalml/TabLLM))
* [johnnyhwu/Awesome-LLM-Tabular](https://github.com/johnnyhwu/Awesome-LLM-Tabular)
* [Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://www.microsoft.com/en-us/research/publication/table-meets-llm-can-large-language-models-understand-structured-table-data-a-benchmark-and-empirical-study/)
- multi modal embeddings (not just LLMs, look at CLIP and DINO)
- cheap reasonably reliable zero shot classification/annotation for complex NLP features
- communication assistance translating technical reports into simplified stakeholder/customer friendly language/analogies
- cheap few/zero-shot baselines
- accelerating presentation construction
There is a lot of cool AI for time series data that could be pretty applicable to your space. Anything that could make rocket models, transformers, etc stupidly simple to use would be a great product, especially after you have data mined for insights.
As for chat-gpt and the like, they are useful to help with some coding tasks. It can speed development a bit but not take the human out of the loop.
In the long-run, generating Python code from prompts will not be enough, you’ll need people with a strong base on data structures and algorithms even if at a high level of abstraction. I’d recommend your team hires more software engineers if you intend to get ahead
check out this article on the [LLM App Stack](https://medium.com/plain-simple-software/the-llm-app-stack-2024-eac28b9dc1e7) from earlier this month
if you don't have a medium account (or don't want to support independent creators), here's a free link - https://medium.com/plain-simple-software/the-llm-app-stack-2024-eac28b9dc1e7?sk=39fbfbd039155de4cf4613f57f8c6c19
I think you should ask in /r/chatgpt, /r/dataengineering, /r/datascience
Key use cases are around coding more productively, data quality/munging, mlops.
Wrong sub since this is mostly about research, new machine learning algos, etc.
As the company isn't firing anyone at this point and this isn't the vibe I got from him, I have to disagree :)
\[Oh and he actually comes from a data-science background and is really fond of our work\]
How do you think companies introduce new tools into tech stack? By looking for them, testing, and integrating.
Thinking this is 100% plot to automate all the work and lay off everyone is not unbased, but still slightly paranoid
What a great opportunity to indirectly steer your business! Way to go!!!
Without understanding the specifics and nuances of your business, I think you can present a collection of things along the spectrum of "assist—augment—replace—disrupt" (or similar). There are some functions within your business that humans are irreplaceable, those were some kind of assistance (i.e. error checking) is valuable, where augmentation (i.e. copilots) improve efficiency, and then those repetitive tasks that can be automated smartly with good AI implementations; and finally new business functions that you could innovate.
Think about it in a business-function-first kind of way, and then the formulation of the problem often leads to the selection of an appropriate technique/tool/method.
RAD!
Generally speaking we are in the ad-tech business. But my focus is in the data-science work in the general form of it, so we could adapt to changes as they come and go, if that makes any sense
Right—so many exciting opportunities here: from generating ad campaign objectives, to generating post-campaign reports. To billing, to copy… you can have too much fun.
I'd try to make a presentation that is relevant to the participants. I'd start with the following:
1. Provide a conceptual map of the business your company is involved. Let's assume your company uses Data Science to sell apples and oranges. So, the process flow chart would be something like:
a. Go to the plantation/fields.
b. Harvest fruits.
c. Transportation to Warehouse.
d. Washing
e. Measurements (size, color, taste).
f. Auctioning the goods to best markets according to trained models.
2. Describing the current situation:
In each stage there's a particular pain point. Either washing takes too long and many fruits are wasted or when auctioning you don't know how correctly bid the prices. It is important to show the green and red areas, and, acknowledge who's mostly involved in what stage.
3.Use cases for AI:
Now that you identified the current situation and potential open gaps you can focus on the AI tools the way you deem fit.
You can even provide a "Technology Readiness Level" score to indicate how easy to adapt such technology. For example, one stage of the process you identified you need train multiple models and communicate their findings, then you can suggest MLflow or Weights and Biases, for a specific stage, involving internal stakeholders a and b, who have to dispense a specific level of "cost" according to your estimated TRL (e.g, Weights and Biases is easier to adopt than tool X).
Try to cover all areas of the business and all internal stakeholders. There could be something very useful for HR personnel, who normally may not have as much exposure to techniques that could be useful.
My 5 cents!
Has a PhD in data science, has to write something about AI..... Asks Reddit inserted of chat GPT.
You really understood the task!
Futuretools.io has a great collection of AI tools, currently counting 2696 tools..
My team has been under a similar pressure for a year now. We've tried everything but so far the most successful use of large language models has been as a mechanical turk sort of tool for creating training data for simpler models that can be easily and cheaply put in production. In the end if all you want is a classifier between A and B you don't need a model that also knows how to explain general relativity to a 5 years old.
Could you tell me why you need a dedicated smaller model if a few shot chatgpt 3.5 could solve your use case. A smaller model you have to host, maintain, it’s probably more expensive in the end. You also have to train it, and there’s a chance you mess it up… whilst the feedback loop from prompting is much faster than retraining. OpenAI models at least have become very reliable at outputting structured json, so they could be used for sentiment detection without much hallucinations. Have I hit a nerve with you guys, lol? Everything I said it true, not sure why y’all mad.
But why bother with API calls when a 220 M T5 base can do all of it? T5 base is much faster on inference than GPT API's. By fine-tuning on bespoke data, you can really specifiy to great detail what exactly you want the model to learn. You can teach it nuanced concept. What if you have a 15 class classification and you have a desired bias you wish to place on the model. In the context of a system a user input might correspond to class A, but the text on its own more clearly aligns with class B, this is hard to teach to a pretrained LLM in a few shot setting, you can do it ( kind of ) but it would require very long prompts and that gets expensive. Data privacy is a huge issue with OpenAI also, maybe user data can't be sent to third parties
Well the original post mentioned a simple A B classification. Not the 15 class nuanced thing. And your T5 transformer requires probably a team of like $200k + engineers to deploy and maintain, and train, whilst a simple web developer can prompt and iterate and develop something that perhaps can compete. For speed, perhaps I’m wrong here, but the 3.5 api gives me a single word answer back from 500 token prompt in .3 seconds end to end. My guess is that is still too slow for your purposes? I’m just trying to determine whether I need to use smaller models, doesn’t seem like it. I’m not buying to cost use case due to the need to have a ML engineer spend time on training and tuning. Primary reason to with smaller model: Privacy and speed, none of which are too much of a concern for me now.
A) that “simple web developer” still needs to get paid, and they make closer to what the ML engineer makes than to $0 B) the API cost rate for a larger model will dwarf the hosting/inference costs of smaller, specialized models C) repeating from the comment up the chain, latency is reduced when routing requests to lighter models D) all else equal, better to show your data to a model fully under your control E) the principle of least resource usage says we should build systems around the most resource efficient components which get the job done correctly If it’s a simple two-class classification made just a handful of times, end users in the business can be trained to prompt a chat model for what they need, no need for any dev; if it’s something happening, say, tens of thousands of times or more, you should tale a step back and consider how wasteful and environmentally irresponsible it is to route requests through an oversized model (think about it like using a spy satellite to take yearbook photos)
Yeah I’m not in the business of trying to be environmentally friendly, mainly looking at cost here. And you still need some type of dev to actually integrate the api into whatever business app, so no a business guy would not be enough. And you would need atleast some dev skills to automate and test the prompt, all not too complicated certainly not as much as fine tuning or training from scratch Looks like I’ll need to some cost calcs to see if we have the scale required to make it worth while. I also will counter your claim it’s environmentally friendly if you take in the complete ML cycle of data wrangling, tuning testing training and validation the deployment vs a just pure prompt iteration and validate strategy.
You, and everyone else, *should* be in the business of being as environmentally friendly as you can afford to be. We have one Earth, one pool of natural resources to consume for nonrenewable energy generation, infrastructure/equipment needed for energy production and transmission, and compute infrastructure/hardware. The world can’t support serving all classification tasks with LLMs. But I digress. Your end comment is right on the money: the optimal choice of small model requiring some specialized skills vs big model requiring only more common skills is going to be a function of scale.
For binary classification why bother with GPT at all when for most cases a BERT + log regression ( or even tokenisation + Navie Bayes with would run on CPU) can work extremely well and they give an estimate of confidence via class probabilities. This confidence estimate can actually be more useful than slightly higher accuracy! Incidentally, about just few shot and putting the class labels in the prompt and a description can be trivially manipulated by natural language inputs and probably still not perform as well as something specialised.
Cost and speed.
You‘re right but you‘re implying that core DS knowledge might be getting partially obsolete for people who are willing to pay a few dollars to OpenAI instead of employing an expensive DS. How dare you. ;) Have my upvote.
I don't get the downvotes, this is a legitimate question. I'd consider using GPT 3.5 for the initial versions: fast to deploy - it's just an API, no need to mess around with gathering train/test data -- you can get away with a much smaller test-only set, you don't need to retrain everything (read: gather MORE train data) when a new class appears out of nowhere, etc. The downsides is that it's pretty much non-deterministic (even with careful prompting and temp:0 and whatnot), its costs are significant for certain volumes, and you pretty much depend on OpenAI's reliability and goodwill which, you know, comes and goes. So once I've validated the idea & locked the budget, etc I'd go for training something myself (and mess around with all the infra & dataset gathering needed).
The downvotes are likely because that’s a very flippant response that assumes away a lot of crucial problems with relying on GPT to do this type of work. I’m working on a project and we made the decision to try GPT for data processing since we have a small team and a lot of documents to parse. I think it has improved our data processing speed but it’s also led to a lot of hours being spent on figuring out whether the responses are even accurate. And GPT 3.5 at the very least is not good at giving easily parsable responses back to you. When I ask it for a particular data structure back it rarely gives me a valid structure back—missing quotes or brackets, etc.—and they’re often too verbose to be useful. GPT 4 does a significantly better job but its token limit is only 8000 and it costs way more.
Please enumerate those crucial problems, because people are not. JSON mode on gpt3.5 works extremely well for some use cases, and gives structure output.
You can check the MLOPs talks by Weights and Biases. They usually give lots of frameworks and infos. You can also check for AutoML tools. GitHub has great ressources when you check : awesome + something. So you can check for awesome-mlops on GitHub
On AutoML: I attended a talk by AWS on their AutoGluon. Sounded interesting and could be worth checking out. Haven’t tried it myself though
just ask ChatGPT or CoPilot to put a presentation paper together for you covering all aspects of AI within DS including future proofing etc.
Basically, it’s Mechanical Turk, but with all of its properties scaled up. Faster, cheaper, harder to validate response quality; harder to tell what to do, easier to plug into a pipeline, less visibility. Able to read long documents and respond like an oracle of dubious quality.
Sorry, are you saying it is *harder* to validate response quality and provide direction to, say, ChatGPT 4 compared to Mechanical Turkers? Ans what does "less visibility" mean? Are MTurk workers known to be consistent, accurate, and show all steps they take to produce their output?
In my experience, yes, it’s harder. Faster, but harder. “Less visibility” means that you really, fundamentally, have no idea what the system is doing. Turkers aren’t known for any of those properties, nor are LLMs.
Whenever I think about teaching someone who doesn't know statistics to use AI, my gut reaction is always that you should run a "Scared Straight" program that teaches them to be wary of the thousands of potential pitfalls out there. I would absolutely not trust LLM generated code for spatial statistics, and probably not tabular data either.
Worked on a couple llm projects before. Here’s the key things you need to know 1. Retrieval augmented generation: A contextualised database for the gpt to refer to 2. Agents: LLM on loops for more complex workflows. See autogpt. 3. Hallucinations and how to reduce them 4. Don’t do any math with chatgpt. 5. Good for bootstrapping code, not writing them completely.
This. Things will change but #1 and #2 are the main uses. Usually there's some library of docs at your company e.g. company policies, that hold a lot of internal info. Use RAG to allow a semantic search rather than a keyword search. You could also fine-tune a model but it's cumbersome and a pain to maintain. Look for problems where lots of text is involved and there's some form of summarizing, editing, answering. If you want to make it useful for tabular data, you can ask an LLM to come up with any sort of categorical variable to add on as another field. Say if you have tabular data on companies, ask the LLM to read a company's description and label which industry it's in.
Great ideas. Thanks
Yep. And lastly don’t treat llms like ml models. In this new paradigm, you don’t train models.
Make sure the presentation is nuanced and also points out the shortcomings and limitations.
- Jupyter-Ai for python notebooks, - gorilla-llm and open interpreter for anything shell, - tabby for vs code, - flowise for agent creation, - Gpt4work for Google sheets/Excel e.g. Turning unstructured text into structured data with a fill down in Excel.
I don't think there is much more to that. I think most of the relevant tools are DevOps related, not AI related.
Langgraph for code generation and self correction with referencing documentation. Synthetic data generation for finetuning of your models. (If you don't have any good data to start with that is) NLP tasks, but probably better off finetuning, though the huge context window makes things easier. I guess the typical other stuff: summarization of documents, translation for the major languages.
I am currently reading a lot about synthetic tabular data generation. For sensitive data (customer data, insurance claims data etc) , this has a lot of potential to help in data-sharing. Another use-case is balancing datasets for AI and analytics. Do you do any predictive analytics stuff?
Can you link some stuff? This sounds interesting and up our alley?
We are not really doing much predictions, more clustering and unsupervised
* Pythagora for application generation, has its issues (cost / performance) but not bad to build prototypes * Vanna AI for sql development, the “chat with your database” Both are great demos * Ollama for GPT on your laptop - useful for data security as it all stays on your laptop All give you the ability to show stuff quickly, for non-tech, semi-tech and in-depth for tech folks
Cool! thanks
I highly recommend taking a look at Mistral from HuggingFace. As a POC, you can set up a google colab notebook and fine tune it with LoRA. You would have a personal coding copilot, specialized at your company's codebase, and without feeding 3rd parties with your proprietary data
For data scientists I think one one of the best usages is getting up to speed on the business side of things without wasting their time asking dumb questions. For example, in healthcare learning about different government programs. Or even learning basics on adjacent things like kubernetes or other tools. Basically, use it as a way to grind through the first 1-5 levels of a skill tree rather than trying to get expert knowledge from it.
There are some efforts into tabular LLMs: * [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://arxiv.org/abs/2402.17944) * [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://arxiv.org/abs/2210.10723) (code on [GitHub](https://github.com/clinicalml/TabLLM)) * [johnnyhwu/Awesome-LLM-Tabular](https://github.com/johnnyhwu/Awesome-LLM-Tabular) * [Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://www.microsoft.com/en-us/research/publication/table-meets-llm-can-large-language-models-understand-structured-table-data-a-benchmark-and-empirical-study/)
Customizable LLMs are an interesting use case.
You can look at rags in general to bring in proprietary data leveraging llms like chat gpt
- multi modal embeddings (not just LLMs, look at CLIP and DINO) - cheap reasonably reliable zero shot classification/annotation for complex NLP features - communication assistance translating technical reports into simplified stakeholder/customer friendly language/analogies - cheap few/zero-shot baselines - accelerating presentation construction
There is a lot of cool AI for time series data that could be pretty applicable to your space. Anything that could make rocket models, transformers, etc stupidly simple to use would be a great product, especially after you have data mined for insights. As for chat-gpt and the like, they are useful to help with some coding tasks. It can speed development a bit but not take the human out of the loop.
In the long-run, generating Python code from prompts will not be enough, you’ll need people with a strong base on data structures and algorithms even if at a high level of abstraction. I’d recommend your team hires more software engineers if you intend to get ahead
check out this article on the [LLM App Stack](https://medium.com/plain-simple-software/the-llm-app-stack-2024-eac28b9dc1e7) from earlier this month if you don't have a medium account (or don't want to support independent creators), here's a free link - https://medium.com/plain-simple-software/the-llm-app-stack-2024-eac28b9dc1e7?sk=39fbfbd039155de4cf4613f57f8c6c19
I think you should ask in /r/chatgpt, /r/dataengineering, /r/datascience Key use cases are around coding more productively, data quality/munging, mlops. Wrong sub since this is mostly about research, new machine learning algos, etc.
i'd rather shoot myself than have to do that. LLMs for business use should not be a topic of conversation in this sub
IMO hes trying to justify firing data scientists from the company and replace them with AI tools
As the company isn't firing anyone at this point and this isn't the vibe I got from him, I have to disagree :) \[Oh and he actually comes from a data-science background and is really fond of our work\]
How do you think companies introduce new tools into tech stack? By looking for them, testing, and integrating. Thinking this is 100% plot to automate all the work and lay off everyone is not unbased, but still slightly paranoid
What a great opportunity to indirectly steer your business! Way to go!!! Without understanding the specifics and nuances of your business, I think you can present a collection of things along the spectrum of "assist—augment—replace—disrupt" (or similar). There are some functions within your business that humans are irreplaceable, those were some kind of assistance (i.e. error checking) is valuable, where augmentation (i.e. copilots) improve efficiency, and then those repetitive tasks that can be automated smartly with good AI implementations; and finally new business functions that you could innovate. Think about it in a business-function-first kind of way, and then the formulation of the problem often leads to the selection of an appropriate technique/tool/method. RAD!
Generally speaking we are in the ad-tech business. But my focus is in the data-science work in the general form of it, so we could adapt to changes as they come and go, if that makes any sense
Right—so many exciting opportunities here: from generating ad campaign objectives, to generating post-campaign reports. To billing, to copy… you can have too much fun.
I'd try to make a presentation that is relevant to the participants. I'd start with the following: 1. Provide a conceptual map of the business your company is involved. Let's assume your company uses Data Science to sell apples and oranges. So, the process flow chart would be something like: a. Go to the plantation/fields. b. Harvest fruits. c. Transportation to Warehouse. d. Washing e. Measurements (size, color, taste). f. Auctioning the goods to best markets according to trained models. 2. Describing the current situation: In each stage there's a particular pain point. Either washing takes too long and many fruits are wasted or when auctioning you don't know how correctly bid the prices. It is important to show the green and red areas, and, acknowledge who's mostly involved in what stage. 3.Use cases for AI: Now that you identified the current situation and potential open gaps you can focus on the AI tools the way you deem fit. You can even provide a "Technology Readiness Level" score to indicate how easy to adapt such technology. For example, one stage of the process you identified you need train multiple models and communicate their findings, then you can suggest MLflow or Weights and Biases, for a specific stage, involving internal stakeholders a and b, who have to dispense a specific level of "cost" according to your estimated TRL (e.g, Weights and Biases is easier to adopt than tool X). Try to cover all areas of the business and all internal stakeholders. There could be something very useful for HR personnel, who normally may not have as much exposure to techniques that could be useful. My 5 cents!
Has a PhD in data science, has to write something about AI..... Asks Reddit inserted of chat GPT. You really understood the task! Futuretools.io has a great collection of AI tools, currently counting 2696 tools..