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
Introduction xiii
Chapter 1 Big Data and Artificial Intelligence 1
Data Is the New Oil and AI Is the New Electricity 1
Rise of the Machines 4
Exponential Growth in Processing 4
A New Breed of Analytics 5
What Makes AI So Special 7
Applications of Artificial Intelligence 8
Building Analytics on Data 12
Types of Analytics: Based on the Application 13
Types of Analytics: Based on Decision Logic 17
Building an Analytics-Driven System 18
Summary 21
Chapter 2 Machine Learning 23
Finding Patterns in Data 23
The Awesome Machine Learning Community 26
Types of Machine Learning Techniques 27
Unsupervised Machine Learning 27
Supervised Machine Learning 29
Reinforcement Learning 31
Solving a Simple Problem 31
Unsupervised Learning 33
Supervised Learning: Linear Regression 37
Gradient Descent Optimization 40
Applying Gradient Descent to Linear Regression 42
Supervised Learning: Classification 43
Analyzing a Bigger Dataset 48
Metrics for Accuracy: Precision and Recall 50
Comparison of Classification Methods 52
Bias vs. Variance: Underfitting vs. Overfitting 57
Reinforcement Learning 62
Model-Based RL 63
Model-Free RL 65
Summary 70
Chapter 3 Handling Unstructured Data 71
Structured vs. Unstructured Data 71
Making Sense of Images 74
Dealing with Videos 89
Handling Textual Data 90
Listening to Sound 104
Summary 108
Chapter 4 Deep Learning Using Keras 111
Handling Unstructured Data 111
Neural Networks 112
Back-Propagation and Gradient Descent 117
Batch vs. Stochastic Gradient Descent 119
Neural Network Architectures 120
Welcome to TensorFlow and Keras 121
Bias vs. Variance: Underfitting vs. Overfitting 126
Summary 129
Chapter 5 Advanced Deep Learning 131
The Rise of Deep Learning Models 131
New Kinds of Network Layers 132
Convolution Layer 133
Pooling Layer 135
Dropout Layer 135
Batch Normalization Layer 135
Building a Deep Network for Classifying Fashion Images 136
CNN Architectures and Hyper-Parameters 143
Making Predictions Using a Pretrained VGG Model 145
Data Augmentation and Transfer Learning 149
A Real Classification Problem: Pepsi vs. Coke 150
Recurrent Neural Networks 160
Summary 166
Chapter 6 Cutting-Edge Deep Learning Projects 169
Neural Style Transfer 169
Generating Images Using AI 180
Credit Card Fraud Detection with Autoencoders 188
Summary 198
Chapter 7 AI in the Modern Software World 199
A Quick Look at Modern Software Needs 200
How AI Fits into Modern Software Development 202
Simple to Fancy Web Applications 203
The Rise of Cloud Computing 205
Containers and CaaS 209
Microservices Architecture with Containers 212
Kubernetes: A CaaS Solution for Infrastructure Concerns 214
Summary 221
Chapter 8 Deploying AI Models as Microservices 223
Building a Simple Microservice with Docker and Kubernetes 223
Adding AI Smarts to Your App 228
Packaging the App as a Container 233
Pushing a Docker Image to a Repository 238
Deploying the App on Kubernetes as a Microservice 238
Summary 240
Chapter 9 Machine Learning Development Lifecycle 243
Machine Learning Model Lifecycle 244
Step 1: Define the Problem, Establish the Ground Truth 245
Step 2: Collect, Cleanse, and Prepare the Data 246
Step 3: Build and Train the Model 248
Step 4: Validate the Model, Tune the Hyper-Parameters 251
Step 5: Deploy to Production 252
Feedback and Model Updates 253
Deployment on Edge Devices 254
Summary 264
Chapter 10 A Platform for Machine Learning 265
Machine Learning Platform Concerns 265
Data Acquisition 267
Data Cleansing 270
Analytics User Interface 271
Model Development 275
Training at Scale 277
Hyper-Parameter Tuning 277
Automated Deployment 279
Logging and Monitoring 286
Putting the ML Platform Together 287
Summary 288
A Final Word . . . 288
Appendix A References 289
Index 295
