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Keras to Kubernetes: The Journey of a Machine Learning Model to Production




Keras to Kubernetes: The Journey of a Machine Learning Model to Production

Dattaraj Rao

ISBN: 978-1-119-56483-6 May 2019 400 Pages


Build a Keras model to scale and deploy on a Kubernetes cluster

We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, we’re seeing a particular growth in Machine Learning (ML) and Deep Learning (DL) applications. ML is all about learning relationships from labeled (Supervised) or unlabeled data (Unsupervised). DL has many layers of learning and can extract patterns from unstructured data like images, video, audio, etc. 

Keras to Kubernetes: The Journey of a Machine Learning Model to Production takes you through real-world examples of building DL models in Keras for recognizing product logos in images and extracting sentiment from text. You will then take that trained model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will understand the different practical steps involved in real-world ML implementations which go beyond the algorithms.

•    Find hands-on learning examples 

•    Learn to uses Keras and Kubernetes to deploy Machine Learning models

•    Discover new ways to collect and manage your image and text data with Machine Learning

•    Reuse examples as-is to deploy your models

•    Understand the ML model development lifecycle and deployment to production

If you’re ready to learn about one of the most popular DL frameworks and build production applications with it, you’ve come to the right place!


A Word from the Author

Chapter 1: BigData & Artificial Intelligence 

Chapter 2: Machine Learning

Chapter 3: Handling Unstructured Data 

Chapter 4: Deep Learning using Keras 

Chapter 5: Advanced Deep Learning 

Chapter 6: Cutting-Edge Deep Learning Projects 

Chapter 7: AI in the Modern Software World 

Chapter 8: Deploying AI Models as a Microservice 

Chapter 9: Maching Learning Development Lifecycle 

Chapter 10: A Platform for Machine Learning