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Keras to Kubernetes The Journey of a Machine Learning Model to Production 1st edition by Dattaraj Rao ISBN 1119564867 978-1119564867

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ISBN 10: 1119564867
ISBN 13: 978-1119564867
Author: Dattaraj Rao 

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.

Keras to Kubernetes The Journey of a Machine Learning Model to Production 1st Table of contents:

Chapter 1: Big Data and Artificial Intelligence

  • Data Is the New Oil and AI Is the New Electricity

  • Rise of the Machines

  • Exponential Growth in Processing

  • A New Breed of Analytics

  • What Makes AI So Special

  • Applications of Artificial Intelligence

  • Building Analytics on Data

  • Types of Analytics: Based on the Application

  • Types of Analytics: Based on Decision Logic

  • Building an Analytics-Driven System

  • Summary

Chapter 2: Machine Learning

  • Finding Patterns in Data

  • The Awesome Machine Learning Community

  • Types of Machine Learning Techniques

  • Unsupervised Machine Learning

  • Supervised Machine Learning

  • Reinforcement Learning

  • Solving a Simple Problem

  • Unsupervised Learning

  • Supervised Learning: Linear Regression

  • Gradient Descent Optimization

  • Applying Gradient Descent to Linear Regression

  • Supervised Learning: Classification

  • Analyzing a Bigger Dataset

  • Metrics for Accuracy: Precision and Recall

  • Comparison of Classification Methods

  • Bias vs. Variance: Underfitting vs. Overfitting

  • Reinforcement Learning

  • Model-Based RL

  • Model-Free RL

  • Summary

Chapter 3: Handling Unstructured Data

  • Structured vs. Unstructured Data

  • Making Sense of Images

  • Dealing with Videos

  • Handling Textual Data

  • Listening to Sound

  • Summary

Chapter 4: Deep Learning Using Keras

  • Handling Unstructured Data

  • Neural Networks

  • Back-Propagation and Gradient Descent

  • Batch vs. Stochastic Gradient Descent

  • Neural Network Architectures

  • Welcome to TensorFlow and Keras

  • Bias vs. Variance: Underfitting vs. Overfitting

  • Summary

Chapter 5: Advanced Deep Learning

  • The Rise of Deep Learning Models

  • New Kinds of Network Layers

  • Convolution Layer

  • Pooling Layer

  • Dropout Layer

  • Batch Normalization Layer

  • Building a Deep Network for Classifying Fashion Images

  • CNN Architectures and Hyper-Parameters

  • Making Predictions Using a Pretrained VGG Model

  • Data Augmentation and Transfer Learning

  • A Real Classification Problem: Pepsi vs. Coke

  • Recurrent Neural Networks

  • Summary

Chapter 6: Cutting-Edge Deep Learning Projects

  • Neural Style Transfer

  • Generating Images Using AI

  • Credit Card Fraud Detection with Autoencoders

  • Summary

Chapter 7: AI in the Modern Software World

  • A Quick Look at Modern Software Needs

  • How AI Fits into Modern Software Development

  • Simple to Fancy Web Applications

  • The Rise of Cloud Computing

  • Containers and CaaS

  • Microservices Architecture with Containers

  • Kubernetes: A CaaS Solution for Infrastructure Concerns

  • Summary

Chapter 8: Deploying AI Models as Microservices

  • Building a Simple Microservice with Docker and Kubernetes

  • Adding AI Smarts to Your App

  • Packaging the App as a Container

  • Pushing a Docker Image to a Repository

  • Deploying the App on Kubernetes as a Microservice

  • Summary

Chapter 9: Machine Learning Development Lifecycle

  • Machine Learning Model Lifecycle

  • Step 1: Define the Problem, Establish the Ground Truth

  • Step 2: Collect, Cleanse, and Prepare the Data

  • Step 3: Build and Train the Model

  • Step 4: Validate the Model, Tune the Hyper-Parameters

  • Step 5: Deploy to Production

  • Feedback and Model Updates

  • Deployment on Edge Devices

  • Summary

Chapter 10: A Platform for Machine Learning

  • Machine Learning Platform Concerns

  • Data Acquisition

  • Data Cleansing

  • Analytics User Interface

  • Model Development

  • Training at Scale

  • Hyper-Parameter Tuning

  • Automated Deployment

  • Logging and Monitoring

  • Putting the ML Platform Together

  • Summary

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Tags: Dattaraj Rao, The Journey, Machine Learning