Deploy Machine Learning Model Using TensorFlow 2.0 Serving | Full Tutorial

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

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Enjoy AI and happy learning