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Deploy Machine Learning Model Using TensorFlow 2.0 Serving | Full Tutorial

  Рет қаралды 31,404

Prof. Ryan Ahmed

Prof. Ryan Ahmed

Күн бұрын

Google has released a new version of tensorflow, which is Tensorflow 2.0!
For those of you guys who are not familiar with tensorflow, tensorflow is Google’s most powerful open source platform for building and deploying AI and machine learning models.
Tensorflow has a ton of comprehensive tools and libraries that enable any developer or researcher to build scary powerful AI model and deploy them in practice.
Tensorflow 2.0 release is a great for AI developers out there, because it is now easier than ever to develop AI models in few lines of code and to deploy these models in practice.
1. Tensorflow now enable eager execution by default!
Tensorflow now has eager execution by default which means you can evaluate operations immediately.
This will make your life 10 times easier when you build and debug your AI model.
Eager execution means that you can now interact with TF 2.0 line by line in google colab or jupyter notebook without the need to define a graph and run sessions and all the mess we had with tensorflow 1.0.
2. TF 2.0 uses keras as the high level API by default
Keras is super easy to use. Keras syntax is very pythonic and for those of you who have worked with Python before will know that python language is super easy to learn!
This is mind blowing, because have can literally build a mini brain to classify images in 10 lines of code.
3. Tensorboard is now integrated with tensorflow 2.0 and it can be easily called
Tensorboard enable us to track the network progress such as accuracy and loss throughout various epochs along with the graph showing various layers of the network which is pretty incredible!
In addition, tensorboard provides a built-in performance dashboard that can be used to track device placement and help us minimize bottlenecks during model execution and training.
4. Tensorflow enable distributed strategy
This feature makes you develop your model once and then decide how you want to run it, over multiple GPUs or TPUs.
This will dramatically improve the computational efficiency with just two additional lines of code, let me show you how to do it!
There are a ton of new features for Tensorflow 2.0 but I just picked 4 of them to share with you.
Now it’s the best time to be alive, and now It’s the best time to master AI and machine learning, the field is exploding with opportunities and career prospects
If you like the video, please hit like and subscribe for more videos
Enjoy AI and happy learning

Пікірлер: 33
@edwardwang5817
@edwardwang5817 4 жыл бұрын
At last, when you show predictions, the code has mistake i suppose, it's the same variable you uesd😅: ...format( class_names[np.argmax(predictions[i])], y_test[i], class_names[np.argmax(predictions[i])], y_test[i],))
@dilshansandhu2627
@dilshansandhu2627 2 жыл бұрын
Can we use tensorflow serving for scikit learn models?
@nayandharviya1453
@nayandharviya1453 3 жыл бұрын
can you please guide me for deploying multiple models using the tensorflow serving ?
@muhammadshoaibsikander5148
@muhammadshoaibsikander5148 3 жыл бұрын
Great tutorial !!!
@sthembisogumede6756
@sthembisogumede6756 3 жыл бұрын
Hi Sir, best video I've seen so far, could I kindly get this notebook for practice, once again, great job, keep up the good job
@RustuYucel
@RustuYucel 4 жыл бұрын
github or code availability?
@muhammadashirali4982
@muhammadashirali4982 Жыл бұрын
Here's a Question! Why we reshape for Batch. I understand Neural Network want this input shape but why reshaping why not batch runs without this ?
@maroben225
@maroben225 4 жыл бұрын
please can you leave your notebook in the description it is going to be helpful for your followers to keep up and follow better
@boquangdong
@boquangdong 4 жыл бұрын
@@diaamohsen5412 this link is guide for tensorflow 1.x . I need guide for tensorflow 2.0
@alberjumper
@alberjumper 2 жыл бұрын
Hi! I'm trying to deploy a model with Docker tensorflow/serving but training in a different machine. When I train and deploy on the same machine, everything goes fine, but when I try to deploy the model on another machine, I get an error related to the loading of the model on the deployment machine. Does anyone know how to solve this issue? Is it possible to train and deploy on two different machines? Thanks!
@divybadoniya1528
@divybadoniya1528 2 жыл бұрын
please solve this issue I am also getting same error
@chris--tech
@chris--tech 4 жыл бұрын
Actually you are not using tf 2.0, bacause tf.saved_model.simple_save is deprecated in tf 2.0. You are using tf 1.15!
@TheAcolossus
@TheAcolossus 4 жыл бұрын
you can auto convert all tf 1 code to 2
@jaggyjut
@jaggyjut 3 жыл бұрын
Nice but I thought you are going to show how to deploy model to cloud like Azure or Google
@pwnkmrdst
@pwnkmrdst 4 жыл бұрын
Hey everyone, Currently I am creating a tensorflow session and loading up .pb file (graph) to memory. Then making predictions by calling functions, which is pretty quick. If I am doing local inferences which one is better?
@PUBUDUCG
@PUBUDUCG 3 жыл бұрын
Local inferencing is faster if it has a GPU too.
@zyhnn
@zyhnn 4 жыл бұрын
Actually not bad!
@UnboxTeckLife
@UnboxTeckLife 4 жыл бұрын
Great, good details. I am new to TF.I hope the initial build is already captured in different lectures. Can you please share the link for TF2 from the beginning so that i can follow the course from beginning
@imranullah3097
@imranullah3097 2 жыл бұрын
Please shere this notebook with me. I'm try but they give me errors. 😶
@Rukshan918
@Rukshan918 3 жыл бұрын
Title is "Deploy Machine Learning Model Using TensorFlow". That part is few min. Whole other is waste of time.
@chandrabhatt
@chandrabhatt 2 жыл бұрын
👏🏼👍🏻
@biswajitroy_isgpp2343
@biswajitroy_isgpp2343 2 жыл бұрын
hey can i have the notebook
@nguyenanhnguyen7658
@nguyenanhnguyen7658 3 жыл бұрын
This is no where "full" :)...
@benjaminelkrieff6414
@benjaminelkrieff6414 4 жыл бұрын
Nothing new.. this is exactly what is offered by Tensorflow doc. How do you deal with base 64, which is by the way more efficient than numpy array ? And If I have a client written in Node.js, how do I send numpy arrays in Node.js ? I'll be glad to know. Most of the solution in internet are not adaptable to the real world and that's sad.
@pradeepkumar-qo8lu
@pradeepkumar-qo8lu 4 жыл бұрын
Maybe Deserialise it into a dictionary (or a json) and then send it over
@TheAcolossus
@TheAcolossus 4 жыл бұрын
you won't find much models in production because you have to code the front end app which most people dont want to learn
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