Deploy Machine Learning Models to Production
With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform
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Beschreibung
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 examplesand 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
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.
What You Will Learn
- 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
Who This Book Is For
Data engineers, data scientists, analysts, and machine learning and deep learning engineers
Guides you in transitioning from traditional machine learning to machine learning productionization Covers the entire range of deployment options, including Flask, Streamlit, Docker, and Kubernetes Presents the process to wrap and containerize any machine learning model
Autor*in
Pramod Singh
Themen in »Deploy Machine Learning Models to Production«
Machine Learning Deep Learning Production Python Flask Kubeflow Apache Spark MLFlow Kubernetes Docker Google Cloud Platform TensorFlow