TensorFlow Serving - Learn Data Science with Travis

Travis chat

Filters

TensorFlow Serving

TensorFlow Serving is a high-performance serving system designed specifically for machine learning models, particularly in production environments. It is part of TensorFlow Extended (TFX) and simplifies the deployment of machine learning models, allowing for easy integration and management of different model versions. This system supports serving models via gRPC and REST protocols, making it versatile for various applications ²³.

One of the key advantages of TensorFlow Serving is its ability to serve multiple versions of models simultaneously, which is essential for A/B testing and gradual rollouts of new algorithms. It also allows for models to be updated without downtime, enhancing the overall efficiency of machine learning operations ³. Additionally, TensorFlow Serving requires models to be in the SavedModel format, which supports fine-tuning and modifications post-training ³.

To get started with TensorFlow Serving, you can use Docker, which is recommended for ease of use and GPU support. This approach allows you to run TensorFlow Serving on any operating system that supports Docker ⁵.

Getting started with TensorFlow Serving

Towards Data Science

TensorFlow Serving is a part of TensorFlow Extended(TFX) that makes deploying your machine learning model to a server more comfortable than ever. Before Google released TensorFlow Serving, your model…...

Serving TensorFlow models with TensorFlow Serving

Towards Data Science

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments.

Serving an Image Classification Model with Tensorflow Serving

Level Up Coding

This is the second part of a blog series that will cover Tensorflow model training, Tensorflow Serving, and its performance. In the previous post, we took an object oriented approach to train an…

TensorFlow Serving client. Make it slimmer and faster!

Towards Data Science

TensorFlow Serving provides a neat way to deploy and serve the models in the production. I have described the deployment process previously here. Unfortunately, there are two problems I noticed much…

Tensorflow Serving with Docker

Towards Data Science

This article will guide you through how you can build and train a simple CNN model and later use this trained model to be served as an endpoint using Tensorflow Serving. Whether you are an Amateur…

TensorFlow Serving: Deploying Deep Learning Models Just Got Easier!

Analytics Vidhya

So you’ve built your machine learning or deep learning model. Congrats! That’s a significant step towards getting the model to your client or customer. But that’s NOT the final stage in your project…

Deploying Machine Learning models with TensorFlow Serving — an introduction

Towards Data Science

Deploying Machine Learning models with TensorFlow Serving — an introduction Step-by-step tutorial from initial environment setup to serving and managing multiple model versions with TensorFlow Servin...

Serving TensorFlow Models. Serverless

Towards Data Science

Previously I wrote, how to serve models with TensorFlow Serving or wrap it up with Flask for provisioning of a REST API. It is also possible to host the model directly with Flask, which is in many…

Serving TensorFlow model in Scala

Towards Data Science

A reasonable Scala library to work with TensorFlow models was built. It could be used to perform classifications, predictions etc.using a pre-trained TensorFlow model in any Scala/Java application.

Tensorflow Serving by creating and using Docker images

Becoming Human: Artificial Intelligence Magazine

Deep Learning (DL) and for a good amount, Machine Learning (ML) suffers from the lack of a proper workflow that makes things simple for the research to directly translate into production. There’s a…

Deploying Yolo on Tensorflow Serving: Part 1

Analytics Vidhya

I am a machine learning engineer, I have been developing a series of machine learning products like Virtual makeup on android and web, image attribute classification, Machine translation, Realtime DL…...

Serving ML with Flask, TensorFlow Serving and Docker Compose

Analytics Vidhya

A short guide on how to serve your deep learning models in production using Flask, Docker-Compose and Tensorflow Serving In the first part, we built a neural network classifier to predict if a given…