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001432981 0247_ $$a10.1007/978-1-4842-6546-8$$2doi
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001432981 1001_ $$aSingh, Pramod.
001432981 24510 $$aDeploy machine learning models to production :$$bwith Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /$$cPramod Singh.
001432981 260__ $$aBerkeley, CA :$$bApress L.P.,$$c2021.
001432981 300__ $$a1 online resource (161 pages)
001432981 336__ $$atext$$btxt$$2rdacontent
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001432981 347__ $$atext file
001432981 347__ $$bPDF
001432981 5050_ $$aIntro -- 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
001432981 5058_ $$aHuman Brain Neuron vs. Artificial Neuron -- Activation Functions -- Sigmoid Activation Function -- Hyperbolic Tangent -- Rectified Linear Unit -- Neuron Computation Example -- Neural Network -- Training Process -- Role of Bias in Neural Networks -- CNN -- RNN -- Industrial Applications and Challenges -- Retail -- Healthcare -- Finance -- Travel and Hospitality -- Media and Marketing -- Manufacturing and Automobile -- Social Media -- Others -- Challenges -- Requirements -- Conclusion -- Chapter 2: Model Deployment and Challenges -- Model Deployment -- Why Do We Need Machine Learning Deployment?
001432981 5058_ $$aChallenges -- 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
001432981 5058_ $$aDeploying a Machine Learning Model as a REST Service -- Templates -- Deploying a Machine Learning Model Using Streamlit -- Deploying a Deep Learning Model -- Training the LSTM Model -- Conclusion -- Chapter 4: Machine Learning Deployment Using Docker -- What Is Docker, and Why Do We Need It? -- Introduction to Docker -- Docker vs. Virtual Machines -- Docker Components and Useful Commands -- Docker Image -- Dockerfile -- Dockerfile Commands -- Docker Hub -- Docker Client and Docker Server -- Docker Container -- Some Useful Container-Related Commands -- Machine Learning Using Docker
001432981 5058_ $$aStep 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
001432981 506__ $$aAccess limited to authorized users.
001432981 520__ $$aBuild 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.
001432981 588__ $$aDescription based on print version record.
001432981 650_0 $$aMachine learning.
001432981 650_0 $$aPython (Computer program language)
001432981 650_0 $$aOpen source software.
001432981 650_0 $$aComputer programming.
001432981 650_6 $$aApprentissage automatique.
001432981 650_6 $$aPython (Langage de programmation)
001432981 650_6 $$aLogiciels libres.
001432981 650_6 $$aProgrammation (Informatique)
001432981 655_0 $$aElectronic books.
001432981 77608 $$iPrint version:$$aSingh, Pramod.$$tDeploy Machine Learning Models to Production : With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform.$$dBerkeley, CA : Apress L.P., ©2021$$z9781484265451
001432981 852__ $$bebk
001432981 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-6546-8$$zOnline Access$$91397441.1
001432981 909CO $$ooai:library.usi.edu:1432981$$pGLOBAL_SET
001432981 980__ $$aBIB
001432981 980__ $$aEBOOK
001432981 982__ $$aEbook
001432981 983__ $$aOnline
001432981 994__ $$a92$$bISE