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Beginning MLOps with MLFlow = deploy...

Alla, Sridhar.

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Beginning MLOps with MLFlow = deploy models in AWS SageMaker, Google Cloud, and Microsoft Azure /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Beginning MLOps with MLFlow/ by Sridhar Alla, Suman Kalyan Adari.
Reminder of title:	deploy models in AWS SageMaker, Google Cloud, and Microsoft Azure /
Author:	Alla, Sridhar.
other author:	Adari, Suman Kalyan.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xiv, 330 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Getting Started: Data Analysis -- Chapter 2: Building Models -- Chapter 3: What Is MLOps? -- Chapter 4: Introduction to MLFlow -- Chapter 5: Deploying in AWS -- Chapter 6: Deploying in Azure -- Chapter 7: Deploying in Google -- Appendix A: a2ml.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-6549-9
ISBN:	9781484265499

Beginning MLOps with MLFlow = deploy models in AWS SageMaker, Google Cloud, and Microsoft Azure /
Alla, Sridhar.

Beginning MLOps with MLFlowdeploy models in AWS SageMaker, Google Cloud, and Microsoft Azure /[electronic resource] :by Sridhar Alla, Suman Kalyan Adari. - Berkeley, CA :Apress :2021. - xiv, 330 p. :ill., digital ;24 cm.

Chapter 1: Getting Started: Data Analysis -- Chapter 2: Building Models -- Chapter 3: What Is MLOps? -- Chapter 4: Introduction to MLFlow -- Chapter 5: Deploying in AWS -- Chapter 6: Deploying in Azure -- Chapter 7: Deploying in Google -- Appendix A: a2ml.

Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training. The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. You will: Perform basic data analysis and construct models in scikit-learn and PySpark Train, test, and validate your models (hyperparameter tuning) Know what MLOps is and what an ideal MLOps setup looks like Easily integrate MLFlow into your existing or future projects Deploy your models and perform predictions with them on the cloud.

ISBN: 9781484265499

Standard No.: 10.1007/978-1-4842-6549-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Beginning MLOps with MLFlow = deploy models in AWS SageMaker, Google Cloud, and Microsoft Azure /
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245 1 0 $a Beginning MLOps with MLFlow $h [electronic resource] : $b deploy models in AWS SageMaker, Google Cloud, and Microsoft Azure / $c by Sridhar Alla, Suman Kalyan Adari.
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505 0 $a Chapter 1: Getting Started: Data Analysis -- Chapter 2: Building Models -- Chapter 3: What Is MLOps? -- Chapter 4: Introduction to MLFlow -- Chapter 5: Deploying in AWS -- Chapter 6: Deploying in Azure -- Chapter 7: Deploying in Google -- Appendix A: a2ml.
520 $a Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training. The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. You will: Perform basic data analysis and construct models in scikit-learn and PySpark Train, test, and validate your models (hyperparameter tuning) Know what MLOps is and what an ideal MLOps setup looks like Easily integrate MLFlow into your existing or future projects Deploy your models and perform predictions with them on the cloud.
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