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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…

Building a scaleable Deep Learning Serving Environment for Keras models using NVIDIA TensorRT…

Towards Data Science

In a recent project at STATWORX, I’ve developed a large scale deep learning application for image classification using Keras and Tensorflow. After developing the model, we needed to deploy it in a…

How to Run Customized Tensorflow Training in the Cloud

Towards Data Science

You have your Tensorflow code running locally. Now you want to set it up in a production environment for all that extra GPU Power. There are a couple of alternatives out there. The two more popular…

TensorFlow on Arduino

Towards Data Science

I am going to train a toy algorithm and deploy it for inferencing on an Arduino Nano 33 BLE Sense. I am seeking to build and test a shell using the fewest possible components, to be enhanced later…

Python:TensorFlow

Codecademy

TensorFlow is a free and open-source library for artificial intelligence and machine learning created by the Google Brain team. It is primarily used with Python but also supports other programming lan...

TensorFlow 2.0: tf.data API

Analytics Vidhya

If you remember the time when only Queues were available in TensorFlow 1.x as the data structure for ETL pipeline (Extract / Transform / Load), and how it was sometimes difficult to manage some…

Getting started with TensorFlow

Analytics Vidhya

TensorFlow might sound intimidating to some ears. But a closer look tells a different story. In this article we discuss - DAG, Estimator API and TensorBoard

Playing with TensorFlow

Towards Data Science

I’ve wanted to learn more about neural nets and in particular TensorFlow for a while now. I recently had a bit more time to dedicate to it, so began to write about what I had learned and some of the…

Tensorflow Cloud: Local to Distributed

Towards Data Science

TensorFlow Cloud is a python package for a seamless transition from local debugging to distributed training in the cloud using the Google AI platform. TensorFlow Cloud has run API for training model…

Serving Image-Based Deep Learning Models with TensorFlow-Serving’s RESTful API

Towards Data Science

TensorFlow Serving is great for serving deep learning models across a network, and it's even better with a RESTful API. Here's how to do it.

Distributed training with TensorFlow

TensorFlow Guide

tf.distribute.Strategy is a TensorFlow API to distribute training across multiple GPUs, multiple machines, or TPUs. Using this API, you can distribute your existing models and training code with minim...

Deploy Keras Models locally using TensorFlow Serving — TF 2.x

Towards Data Science

I shall cover how to create a basic and super simple Keras Model and how to deploy it on your local machine. This model would definitely be simpler than whatever you plan to deploy (well, I hope…

How to Serve Different Model Versions using TensorFlow Serving?

Towards Data Science

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…