D How successful are ML projects?
Our team just deployed our first ML solution. We have several people with DS/ML certificates, and have done ML in side projects and hackathons, but no one has a degree in stats or math or DS and no one's ever done ML professionally. We regularly consulted multiple DS / ML experts, but never had a dedicated DS on our team. It cost us \~$400K to implement, is expected to save $50K a year, and have operational costs of $20K a year.
​
It seems like pursuing this wasn't worth it. Was this a miscalculation on our part? What's the success rate of the projects you work on? How much do you cost the company vs how much money do you generate (or save)?
​
/r/MachineLearning
https://redd.it/1cxtro6
Our team just deployed our first ML solution. We have several people with DS/ML certificates, and have done ML in side projects and hackathons, but no one has a degree in stats or math or DS and no one's ever done ML professionally. We regularly consulted multiple DS / ML experts, but never had a dedicated DS on our team. It cost us \~$400K to implement, is expected to save $50K a year, and have operational costs of $20K a year.
​
It seems like pursuing this wasn't worth it. Was this a miscalculation on our part? What's the success rate of the projects you work on? How much do you cost the company vs how much money do you generate (or save)?
​
/r/MachineLearning
https://redd.it/1cxtro6
Reddit
[D] How successful are ML projects? : r/MachineLearning
54 votes, 22 comments. 2.9M subscribers in the MachineLearning community. ml.
Beginners please see learnmachinelearning
Beginners please see learnmachinelearning
Cookies are not being stored in the browser. Django Backend and react frontend.
So My backend code is in django and frontend code is in react. Backend has been hosted in render and frontend is not yet hosted. i.e. I work in localhost:3000.
​
Iam using cookies to store session data.
When I login I expect the sessionid and csrf id to be store in the browser, When I tested the API endpoint in POSTMAN It worked fine i.e. it stored the session id and csrf tokein in the cookies and all the other endpoint that required login worked fine.
​
Here is what happened when I integrated react with backend.
​
When I log in cookies are being generated and these are valid cookies, cause I have copy pasted then into postman and they work fine.
​
But after login when I see that no cookies is being stored. So as a result I cannot use other endpoint where login is required.
Here is the configuration of my backend
​
https://preview.redd.it/ni4ymqh6yc4d1.png?width=967&format=png&auto=webp&s=677b995fd173c3f79cc2809cfb7e0538cdb686f9
I have two session engines. django.contrib.sessions.middleware.SessionMiddleware and the one in the screenshot. But nothing has stored the cookie data.
If you want to see anything else I have given my github repo link at the end cd Backend/bacend/backend/backend/settings.py
​
This is the endpoint that will check if the user is logged in or not based
/r/django
[https://redd.it/1d74fww
So My backend code is in django and frontend code is in react. Backend has been hosted in render and frontend is not yet hosted. i.e. I work in localhost:3000.
​
Iam using cookies to store session data.
When I login I expect the sessionid and csrf id to be store in the browser, When I tested the API endpoint in POSTMAN It worked fine i.e. it stored the session id and csrf tokein in the cookies and all the other endpoint that required login worked fine.
​
Here is what happened when I integrated react with backend.
​
When I log in cookies are being generated and these are valid cookies, cause I have copy pasted then into postman and they work fine.
​
But after login when I see that no cookies is being stored. So as a result I cannot use other endpoint where login is required.
Here is the configuration of my backend
​
https://preview.redd.it/ni4ymqh6yc4d1.png?width=967&format=png&auto=webp&s=677b995fd173c3f79cc2809cfb7e0538cdb686f9
I have two session engines. django.contrib.sessions.middleware.SessionMiddleware and the one in the screenshot. But nothing has stored the cookie data.
If you want to see anything else I have given my github repo link at the end cd Backend/bacend/backend/backend/settings.py
​
This is the endpoint that will check if the user is logged in or not based
/r/django
[https://redd.it/1d74fww
R I finetuned GPT-2 and BERT 135,000 times to see if it's fair to pretrain on unlabeled text from the test set
TL;DR: seems fair.
Everyone knows that training on labeled test data is a big no-no if you wanna use that data for evaluation.
But if you pretrain on unlabeled test data, can you still use that data during evaluation?
Let's run an experiment on 25 text classification datasets, two LMs whose pretraining data we're pretty sure are not already contaminated, and across a few settings for the number of training and test observations.
For each dataset, the experiment sanity checks that pretraining on unlabeled text (independent of the test set) helps, i.e., that there's an effect to detect. Call this the pretraining boost. Next, the experiment evaluates the bias from pretraining on unlabeled text from the test set instead of unlabeled independent text. Call this the evaluation bias.
# Results
​
m=50 is based on the Real-world Annotated Few-shot Tasks \(RAFT\) benchmark \(https:\/\/arxiv.org\/abs\/2109.14076\), which also contains the inspiration for this question: “For each task, we release a public training set with 50 examples and a larger unlabeled test set. We encourage unsupervised pre-training on the unlabelled examples…”. The distributions above are those of marginal effects: averaged across 2 LM types, the 25 classification tasks, and their subsamples.
Stretching the intention of “few” in few-shot here. I wanted to
/r/MachineLearning
[https://redd.it/1do2g03
TL;DR: seems fair.
Everyone knows that training on labeled test data is a big no-no if you wanna use that data for evaluation.
But if you pretrain on unlabeled test data, can you still use that data during evaluation?
Let's run an experiment on 25 text classification datasets, two LMs whose pretraining data we're pretty sure are not already contaminated, and across a few settings for the number of training and test observations.
For each dataset, the experiment sanity checks that pretraining on unlabeled text (independent of the test set) helps, i.e., that there's an effect to detect. Call this the pretraining boost. Next, the experiment evaluates the bias from pretraining on unlabeled text from the test set instead of unlabeled independent text. Call this the evaluation bias.
# Results
​
m=50 is based on the Real-world Annotated Few-shot Tasks \(RAFT\) benchmark \(https:\/\/arxiv.org\/abs\/2109.14076\), which also contains the inspiration for this question: “For each task, we release a public training set with 50 examples and a larger unlabeled test set. We encourage unsupervised pre-training on the unlabelled examples…”. The distributions above are those of marginal effects: averaged across 2 LM types, the 25 classification tasks, and their subsamples.
Stretching the intention of “few” in few-shot here. I wanted to
/r/MachineLearning
[https://redd.it/1do2g03
arXiv.org
Pretraining on the Test Set Is All You Need
Inspired by recent work demonstrating the promise of smaller Transformer-based language models pretrained on carefully curated data, we supercharge such approaches by investing heavily in curating...
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