Beginning MLOps with MLFlow: Deploy Models in AWS Sage Maker, Google Cloud, and Microsoft Azure (Record no. 49877)

MARC details
000 -LEADER
fixed length control field 02042 a2200217 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250327102731.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781484284346
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 ALL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Alla, Sridhar
245 ## - TITLE STATEMENT
Title Beginning MLOps with MLFlow: Deploy Models in AWS Sage Maker, Google Cloud, and Microsoft Azure
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc APress
Date of publication, distribution, etc 2021
Place of publication, distribution, etc New York
300 ## - PHYSICAL DESCRIPTION
Extent 330
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Cloud computing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer software
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Google Cloud
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Microsoft Azure
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Adari, Suman Kalyan
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification

No items available.