Intro
Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Introduction to Machine Learning
History
The Last Decade
Rise in Data
Increased Computational Efficiency
Improved ML Algorithms
Availability of Data Scientists
Machine Learning
Supervised Machine Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning
Gradient Descent
Bias vs. Variance
Cross Validation and Hyperparameters
Performance Metrics
Deep Learning

Human Brain Neuron vs. Artificial Neuron
Activation Functions
Sigmoid Activation Function
Hyperbolic Tangent
Rectified Linear Unit
Neuron Computation Example
Neural Network
Training Process
Role of Bias in Neural Networks
CNN
RNN
Industrial Applications and Challenges
Retail
Healthcare
Finance
Travel and Hospitality
Media and Marketing
Manufacturing and Automobile
Social Media
Others
Challenges
Requirements
Conclusion
Chapter 2: Model Deployment and Challenges
Model Deployment
Why Do We Need Machine Learning Deployment?

Challenges
Challenge 1: Coordination Between Stakeholders
Challenge 2: Programming Language Discrepancy
Challenge 3: Model Drift
Changing Behavior of the Data
Changing Interpretation of the New Data
Challenge 4: On-Prem vs. Cloud-Based Deployment
Challenge 5: Clear Ownership
Challenge 6: Model Performance Monitoring
Challenge 7: Release/Version Management
Challenge 8: Privacy Preserving and Secure Model
Conclusion
Chapter 3: Machine Learning Deployment as a Web Service
Introduction to Flask
route Function
run Method

Deploying a Machine Learning Model as a REST Service
Templates
Deploying a Machine Learning Model Using Streamlit
Deploying a Deep Learning Model
Training the LSTM Model
Conclusion
Chapter 4: Machine Learning Deployment Using Docker
What Is Docker, and Why Do We Need It?
Introduction to Docker
Docker vs. Virtual Machines
Docker Components and Useful Commands
Docker Image
Dockerfile
Dockerfile Commands
Docker Hub
Docker Client and Docker Server
Docker Container
Some Useful Container-Related Commands
Machine Learning Using Docker

Step 1: Training the Machine Learning Model
Step 2: Exporting the Trained Model
Step 3: Creating a Flask App Including UI
Step 4: Building the Docker Image
Step 5: Running the Docker Container
Step 6: Stopping/Killing the Running Container
Conclusion
Chapter 5: Machine Learning Deployment Using Kubernetes
Kubernetes Architecture
Kubernetes Master
Worker Nodes
ML App Using Kubernetes
Google Cloud Platform
Conclusion
Index