I am going to watch whole video in 2 days. I am sure i am going to learn everything you taught in this video.. i will comments again my feedback after completion.😊😌
This video taught me a lot, right from the best practices in Data Engineering/Data Science. However, I could not able to assimilate the use of AI as an API. I had built an AI project with front-end thus I could see the results directly, I would be glad to know the use case of the AI as an API like where this can be used. Apologies, if the question doesn't make sense.
Great question! This api can be used in nearly *any* service. This means regardless what your front end or backend is, you can probably use this API. For example, if you have 4 different web applications, they can all use this API to provide predictions for the content. You can see this in action on my Try Django 3.2 series where we integrate Django with an AI microservice.
This is frustrating to follow as I'm encountering a: "WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available." Error. I'm using VS Code from Anaconda.
Thank you for this wonderful video. I followed your tutorial, it worked fine. But for the function "export_inferences", the fastAPI docs gave me "Unrecognized response type; displaying content as text.", the dataset can't be showed. Do you have any idea why it happend?
The model is predicting everything as ham . i have followed each step in this video but the problem presists can anyone help me. The model itself at google colab is giving this result.
Thanks for very useful tutorial! Is there a free object storage available that will work also with boto3? Because as far as I understood all of three suggested solutions charge at least 5 $ / month, and it's unwanted to make an expense just for few files and testing purposes.
okay... so why not pytorch, kedro and bentoml (and mlflow), that's waayyy easier than coding all stuff by hand, just watching this video was exhausting let alone coding all that by hand:p
To people that don’t have experience with higher level math like linear algebra, or algebraic combinatorics (Stochastic gradient dissent should be a concept that you know inside and out ) then this is not your video, if you’re looking for a quick fix go to hugging face there you can use sckitlearn and gardio
you can use above api in comment system to approve and unapprove spam comments automatically based on percentage, make money selling feat or make softw like Disqus comment, duh ML, AI is future...
Part 2 is up! ua-cam.com/video/nTdMjFcK3SM/v-deo.html
Only 42 mins in and I know you're a life saver
I am going to watch whole video in 2 days. I am sure i am going to learn everything you taught in this video.. i will comments again my feedback after completion.😊😌
We are loving your FastAPI and Cassandra videos!! We want more......
OH MY GOD!... I just saw this on my recommended. I have not seen any video yet. SUBBED!!!. I hope to learn more as I dig in
Man, after I learned from your tutorials I got a job on upwork thank you
So cool! Nice work
Dude I saved 15% on my car insurance by switching to Geico thanks ;)
Sick tutorials recently.. thanks programing brother 🧬
Such a great tutorial! Can't wait to start part 2 tomorrow. Thanks so much man.
Nice content to pass the weekend. Thanks!
Started ML ... Thank you.
Please make a tutorial for OpenCV AI model with Django Rest API too.
Yeah, that’s right!
FYI, model.predict is not thread-safe and hence, cannot be usef within fastAPI like that. A correct way is to use tensorflow serving
Thank you, haven't watched it yet but liked it, I will watch it this weekend and run the codes using Pytorch maybe image models (if possible) :).
Really? You’ll have to let us all know how that worked out, I’m kind of curious
Great work, lucid !
Thank you so much for doing this! There is hope in this sad world!
That sounds like a really cool idea. Thank you
Thank you
This video taught me a lot, right from the best practices in Data Engineering/Data Science. However, I could not able to assimilate the use of AI as an API. I had built an AI project with front-end thus I could see the results directly, I would be glad to know the use case of the AI as an API like where this can be used. Apologies, if the question doesn't make sense.
Great question!
This api can be used in nearly *any* service. This means regardless what your front end or backend is, you can probably use this API.
For example, if you have 4 different web applications, they can all use this API to provide predictions for the content.
You can see this in action on my Try Django 3.2 series where we integrate Django with an AI microservice.
@@CodingEntrepreneurs Makes sense, perfect. Thanks!
straightforward, nice!
Thank you for your valuable information, I appreciate your efforts, wish you the more success and the best
Long time no see!
Please make a tutorial on machine learning for beginners
Thank you sir
Great content....
Sir, when we expect part 2 of this video?
Description link says, private video.
Please update sir
It’s here!! ua-cam.com/video/nTdMjFcK3SM/v-deo.html
@@CodingEntrepreneurs Thank You!!!
Thanks!
Gold Content
Great video 😎
This is frustrating to follow as I'm encountering a:
"WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available."
Error.
I'm using VS Code from Anaconda.
Thank you for this wonderful video. I followed your tutorial, it worked fine. But for the function "export_inferences", the fastAPI docs gave me "Unrecognized response type; displaying content as text.", the dataset can't be showed. Do you have any idea why it happend?
Perhaps turn your stream response into a string.
Also thank you!
Ah UCL .. The university that nearly drove me crazy doing a master degree :)
IEEE dataport datasets are also fun and interesting, just FYI
What if I need the user to upload his dataset, choose the model and then get back the model trained? How I would do that?
The model is predicting everything as ham . i have followed each step in this video but the problem presists can anyone help me.
The model itself at google colab is giving this result.
like like like.......😍
Amazing tutorial, can you please make a tutorial on machine learning pipeline with datasets storage in Cassandra?
Yes it's coming! Not in part 2 but in a future series!
Thanks for very useful tutorial!
Is there a free object storage available that will work also with boto3? Because as far as I understood all of three suggested solutions charge at least 5 $ / month, and it's unwanted to make an expense just for few files and testing purposes.
Linode.com/cfe
or
do.co/cfe-sh
For some free credits
@@CodingEntrepreneurs thanks a lot
Hi! Thanks for the huge tutorial. Eagerly waiting for the part 2. For some reason, part two link is not accessible (private link).
Soon it will be published!!
It’s here! ua-cam.com/video/nTdMjFcK3SM/v-deo.html
@@CodingEntrepreneurs Thank you very much!
Why have u chosen fast api over django?
amazing video. MLops is silver bullet
Awesome ... +1000(y)
I read this as "Alas, an API"
Haha!
okay... so why not pytorch, kedro and bentoml (and mlflow), that's waayyy easier than coding all stuff by hand, just watching this video was exhausting let alone coding all that by hand:p
To people that don’t have experience with higher level math like linear algebra, or algebraic combinatorics
(Stochastic gradient dissent should be a concept that you know inside and out ) then this is not your video, if you’re looking for a quick fix go to hugging face there you can use sckitlearn and gardio
Can we get the code ?
Check the description
I make with this idea hehe but I didn't know how to
Everyone teaching ML but no one knows how to earn money from it
you can use above api in comment system to approve and unapprove spam comments automatically based on percentage, make money selling feat or make softw like Disqus comment, duh ML, AI is future...
@@nepalcodetv6298 who will buy that ? Google is it ?