Incorporating web app with Tensorflow serving image
#Is docker and kubernetes the same how to
In this section, we will discuss the most essential docker API needed in taking our machine learning project to production and also see how to orchestrate our app with docker-compose. Model.save(file_path, saved_format=’tf’) create some files which are need for Tensorflow serving. The timestamp at the moment of saving the model is obtained and used to create an inner folder in saved_models/ and then the model is saved into the folder. In the code above we infuse the idea of versioning using the time module. Model.fit(data,label,batch_size= 2, epochs= 5)Īfter creating and training the model will need to save the model in a way it can be servable using TensorFlow serving, hence we won’t be saving just the model weight.
Let’s create a file named model.py and input the code below: Import TensorFlow as tfĭata=tf.constant(,]) The model is a AND logic gate model, and since the main focus of the article is not on how to create a model or neither how to train it, this part will be less explained. In this article section, we will be creating a simple ML model, which we will use to solidify the concept to be introduced. NOTE: You can obtain the code used for the article here.
In this article, we will also follow a project-based method, which will make it possible for you to just port the ideas and code shown directly into your machine learning project.
#Is docker and kubernetes the same software
“…so hard, the rare data scientist who can also develop quality software and play engineer is called a unicorn!”- Elle O’Brien Hence, if you are just like me, ready to up your game and add one of the tools to become a Unicorn data scientist, as described by Elle O’Brien in this article, then this article is for you. The journey and curiosity led to this article. Along the line, I discovered I need to know more (maybe just a little) of Kubernetes needed for deploying, orchestrating, and scaling machine learning apps. TensorFlow extended and Kubeflow (Kubernetes made easier for machine learning projects). I started diving deep into TensorFlow serving.
📚 Machine Learning Model Management in 2021 and Beyond – Everything That You Need to Know
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