| Description |
1 online resource (161 pages) |
|
text file |
|
PDF |
| Contents |
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 |
| Summary |
Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. You will: Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks. |
| Subject |
Machine learning.
|
|
Python (Computer program language)
|
|
Open source software.
|
|
Computer programming.
|
|
Apprentissage automatique. |
|
Python (Langage de programmation) |
|
Logiciels libres. |
|
Programmation (Informatique) |
|
computer programming. |
|
Computer programming |
|
Machine learning |
|
Open source software |
|
Python (Computer program language) |
| Other Form: |
Print version: Singh, Pramod. Deploy Machine Learning Models to Production : With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform. Berkeley, CA : Apress L.P., ©2021 9781484265451 |
| ISBN |
9781484265468 |
|
1484265467 |
|
9781484265475 (print) |
|
1484265475 |
| Standard No. |
10.1007/978-1-4842-6546-8 doi |
|
More Resources