Data Science & Developer Roadmaps with Chat & Free Learning Resources

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 and algorithms while maintaining a consistent server architecture and APIs 2.

One of the key features of TensorFlow Serving is its ability to serve models via gRPC and REST protocols, enabling them to function as web services without the need for additional applications. This allows for multiple versions of multiple models to be served simultaneously, which is beneficial for A/B testing and gradual rollouts of new models 3.

To use TensorFlow Serving, models must be saved in TensorFlow’s SavedModel format, which allows for fine-tuning and modifications post-training. The recommended approach for deployment is through Docker, as it provides an easy way to manage dependencies and ensure compatibility, especially when using GPU support 35.

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…...

Read more at Towards Data Science | Find similar documents

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.

Read more at Towards Data Science | Find similar documents

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…

Read more at Level Up Coding | Find similar documents

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…

Read more at Towards Data Science | Find similar documents

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…

Read more at Towards Data Science | Find similar documents

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…

Read more at Analytics Vidhya | Find similar documents

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...

Read more at Towards Data Science | Find similar documents

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…

Read more at Towards Data Science | Find similar documents

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.

Read more at Towards Data Science | Find similar documents

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…

Read more at Becoming Human: Artificial Intelligence Magazine | Find similar documents

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…...

Read more at Analytics Vidhya | Find similar documents

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…

Read more at Analytics Vidhya | Find similar documents