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# 734019
USD 39.99 (Book Not in Ready Stock, will take 45-60 days to source and dispatch)
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Beginning MLOps with MLFlow = Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure

Author :  Sridhar Alla; Suman Kalyan Adari

Product Details

  • Country United States
  • Publisher Apress
  • ISBN9781484265482
  • FormatPaperBack
  • LanguageEnglish
  • Year of Publication2021
  • Bib. InfoXIX, 317 p. 267 illus., 266 illus. in color.
  • CategoriesComputer Science
  • Product Weight528 gms.
  • Shipping Charges(USD)

Product Description

Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. ‚ÄčThis 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.
What You Will Learn Perform basic data analysis and construct models in scikit-learn and PySparkTrain, test, and validate your models (hyperparameter tuning)Know what MLOps is and what an ideal MLOps setup looks likeEasily integrate MLFlow into your existing or future projectsDeploy your models and perform predictions with them on the cloud
Who This Book Is For
Data scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models

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