Serving TensorFlow models with TensorFlow Serving

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.

📖 Introduction

Currently there are a lot of different solutions to serve ML models in production with the growth that **MLOps **is having nowadays as the standard procedure to work with ML models during all their lifecycle. Maybe the most popular one is TensorFlow Serving developed by TensorFlow so as to server their models in production environments.

This post is a guide on how to train, save, serve and use TensorFlow ML models in production environments. Along the GitHub repository linked to this post we will prepare and train a custom CNN model for image classification of The Simpsons Characters Data dataset, that will be later deployed using TensorFlow Serving.

So as to get a better understanding on all the process that is presented in this post, as a personal recommendation, you should read it while you check the resources available in the repository, as well as trying to reproduce it with the same or with a different TensorFlow model, as “practice makes the master”.

alvarobartt/serving-tensorflow-models

#deep-learning #tensorflow-serving #tensorflow

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Serving TensorFlow models with TensorFlow Serving

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.

📖 Introduction

Currently there are a lot of different solutions to serve ML models in production with the growth that **MLOps **is having nowadays as the standard procedure to work with ML models during all their lifecycle. Maybe the most popular one is TensorFlow Serving developed by TensorFlow so as to server their models in production environments.

This post is a guide on how to train, save, serve and use TensorFlow ML models in production environments. Along the GitHub repository linked to this post we will prepare and train a custom CNN model for image classification of The Simpsons Characters Data dataset, that will be later deployed using TensorFlow Serving.

So as to get a better understanding on all the process that is presented in this post, as a personal recommendation, you should read it while you check the resources available in the repository, as well as trying to reproduce it with the same or with a different TensorFlow model, as “practice makes the master”.

alvarobartt/serving-tensorflow-models

#deep-learning #tensorflow-serving #tensorflow

Deployment of a TensorFlow model to Production using TensorFlow Serving

Learn step by step deployment of a TensorFlow model to Production using TensorFlow Serving.

You created a deep learning model using Tensorflow, fine-tuned the model for better accuracy and precision, and now want to deploy your model to production for users to use it to make predictions.

TensorFlow Serving allows you to

• Easily manage multiple versions of your model, like an experimental or stable version.
• Keep your server architecture and APIs the same
• Dynamically discovers a new version of the TensorFlow flow model and serves it using (remote procedure protocol) using a consistent API structure
• Consistent experience for all clients making inferences by centralizing the location of the model

The key components of TF Serving are

• Servables: A Servable is an underlying object used by clients to perform computation or inference**. TensorFlow serving represents the deep learning models as one ore more Servables.
• Loaders: Manage the lifecycle of the Servables as Servables cannot manage their own lifecycle. Loaders standardize the APIs for loading and unloading the Servables, independent of the specific learning algorithm.
• Source: Finds and provides Servables and then supplies one Loader instance for each version of the servable.
• Managers: Manage the full lifecycle of the servable: Loading the servable, Serving the servable, and Unloading the servable
• TensorFlow Core: Manages lifecycle and metrics of the Servable by making the Loader and servable as opaque objects

#tensorflow-serving #deep-learning #mnist #tensorflow #windows-10

How to Serve Different Model Versions using TensorFlow Serving

This article explains how to manage multiple models and multiple versions of the same model in TensorFlow Serving using configuration files along with a brief understanding of batching.

You have TensorFlow deep learning models with different architectures or have trained your models with different hyperparameters and would like to test them locally or in production. The easiest way is to serve the models using a Model Server Config file.

A Model Server Configuration file is a protocol buffer file(protobuf), which is a language-neutral, platform-neutral extensible yet simple and faster way to serialize the structure data.

#deep-learning #python #tensorflow-serving #tensorflow

Deploying Models to Production with TensorFlow Model Server

Model creation is definitely an important part of AI applications but it is very important to also know what after training. I will be showing how you could serve TensorFlow models over HTTP and HTTPS and do things like model versioning or model server maintenance easily with TF Model Server. You will also see the steps required for this and the process you should follow. We will also take a look at Kubernetes and GKE to autoscale your deployments.

#deep-learning #tensorflow-serving #artificial-intelligence #tensorflow #machine-learning

How TensorFlow Lite Fits In The TinyML Ecosystem

TensorFlow Lite has emerged as a popular platform for running machine learning models on the edge. A microcontroller is a tiny low-cost device to perform the specific tasks of embedded systems.

In a workshop held as part of Google I/O, TensorFlow founding member Pete Warden delved deep into the potential use cases of TensorFlow Lite for microcontrollers.

Further, quoting the definition of TinyML from a blog, he said:

“Tiny machine learning is capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of ways-on-use-case and targeting battery operated devices.”

#opinions #how to design tinyml #learn tinyml #machine learning models low cost #machine learning models low power #microcontrollers #tensoflow latest #tensorflow lite microcontrollers #tensorflow tinyml #tinyml applications #tinyml models