Cover image for Keras to Kubernetes

Keras to Kubernetes

Author Dattaraj Rao
Release Date: 2019/05/01
ISBN: 9781119564836
Topic:
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22
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Book Description

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!

Table of Contents

  1. Cover
  2. Introduction
    1. How This Book Is Organized
    2. Conventions Used
    3. Who Should Read This Book
    4. Tools You Will Need
    5. Summary
  3. CHAPTER 1: Big Data and Artificial Intelligence
    1. Data Is the New Oil and AI Is the New Electricity
    2. Applications of Artificial Intelligence
    3. Summary
  4. CHAPTER 2: Machine Learning
    1. Finding Patterns in Data
    2. The Awesome Machine Learning Community
    3. Types of Machine Learning Techniques
    4. Solving a Simple Problem
    5. Analyzing a Bigger Dataset
    6. Comparison of Classification Methods
    7. Bias vs. Variance: Underfitting vs. Overfitting
    8. Reinforcement Learning
    9. Summary
  5. CHAPTER 3: Handling Unstructured Data
    1. Structured vs. Unstructured Data
    2. Making Sense of Images
    3. Dealing with Videos
    4. Handling Textual Data
    5. Listening to Sound
    6. Summary
  6. CHAPTER 4: Deep Learning Using Keras
    1. Handling Unstructured Data
    2. Welcome to TensorFlow and Keras
    3. Bias vs. Variance: Underfitting vs. Overfitting
    4. Summary
  7. CHAPTER 5: Advanced Deep Learning
    1. The Rise of Deep Learning Models
    2. New Kinds of Network Layers
    3. Building a Deep Network for Classifying Fashion Images
    4. CNN Architectures and Hyper‐Parameters
    5. Making Predictions Using a Pretrained VGG Model
    6. Data Augmentation and Transfer Learning
    7. A Real Classification Problem: Pepsi vs. Coke
    8. Recurrent Neural Networks
    9. Summary
  8. CHAPTER 6: Cutting‐Edge Deep Learning Projects
    1. Neural Style Transfer
    2. Generating Images Using AI
    3. Credit Card Fraud Detection with Autoencoders
    4. Summary
  9. CHAPTER 7: AI in the Modern Software World
    1. A Quick Look at Modern Software Needs
    2. How AI Fits into Modern Software Development
    3. Simple to Fancy Web Applications
    4. The Rise of Cloud Computing
    5. Containers and CaaS
    6. Kubernetes: A CaaS Solution for Infrastructure Concerns
    7. Summary
  10. CHAPTER 8: Deploying AI Models as Microservices
    1. Building a Simple Microservice with Docker and Kubernetes
    2. Adding AI Smarts to Your App
    3. Packaging the App as a Container
    4. Pushing a Docker Image to a Repository
    5. Deploying the App on Kubernetes as a Microservice
    6. Summary
  11. CHAPTER 9: Machine Learning Development Lifecycle
    1. Machine Learning Model Lifecycle
    2. Deployment on Edge Devices
    3. Summary
  12. CHAPTER 10: A Platform for Machine Learning
    1. Machine Learning Platform Concerns
    2. Putting the ML Platform Together
    3. Summary
    4. A Final Word …
  13. APPENDIX A: References
  14. Index
  15. End User License Agreement
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