Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units

TensorFlow Serving

TensorFlow Serving (TFS) is a high-performance server architecture for serving the machine learning models in production. It offers out-of-the-box integration with the models built using TensorFlow.

In  TFS, a model is composed of one or more servables. A servable is used to perform computation, for example:

  • A lookup table for embedding lookups
  • A single model returning predictions
  • A tuple of models returning a tuple of predictions
  • A shard of lookup tables or models

The manager component manages the full lifecycle for the servables including loading/unloading a servable and serving the servable.


The internal architecture and workflow of  TensorFlow Serving is described at the following link:

Installing TF Serving

Follow the instructions in this section to install the TensorFlow ModelServer on Ubuntu using aptitude.

  1. First, add TensorFlow Serving distribution URI as a package source (one-time setup) with the following command...