In this chapter, we learned how to leverage the TensorFlow Serving to serve the models in production environments. We also learned how to save and restore full models or selective models using both TensorFlow and Keras. We built a Docker container and served the sample MNIST example code in the Docker container from the official TensorFlow Serving repository. We also installed a local Kubernetes cluster and deployed the MNIST model to serve from TensorFlow Serving running in Kubernetes pods. We encourage the reader to build upon these examples and try out serving different models. TF Serving documentation describes various options and provides additional information enabling you to explore this topic further.
Python: Advanced Guide to Artificial Intelligence
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Python: Advanced Guide to Artificial Intelligence
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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
Contributors
Preface
Free Chapter
Machine Learning Model Fundamentals
Introduction to Semi-Supervised Learning
Graph-Based Semi-Supervised Learning
Bayesian Networks and Hidden Markov Models
EM Algorithm and Applications
Hebbian Learning and Self-Organizing Maps
Clustering Algorithms
Advanced Neural Models
Classical Machine Learning with TensorFlow
Neural Networks and MLP with TensorFlow and Keras
RNN with TensorFlow and Keras
CNN with TensorFlow and Keras
Autoencoder with TensorFlow and Keras
TensorFlow Models in Production with TF Serving
Deep Reinforcement Learning
Generative Adversarial Networks
Distributed Models with TensorFlow Clusters
Debugging TensorFlow Models
Tensor Processing Units
Getting Started
Image Classification
Image Retrieval
Object Detection
Semantic Segmentation
Similarity Learning
Other Books You May Enjoy
Index
Customer Reviews