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Beginning MLOps with MLFlow - Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure (Paperback, 1st ed.)
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Beginning MLOps with MLFlow - Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure (Paperback, 1st ed.)
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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. What You Will Learn 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 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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