LEADER 03142nam 2200493 450 001 9910483568503321 005 20210320133409.0 010 $a1-4842-6549-1 024 7 $a10.1007/978-1-4842-6549-9 035 $a(CKB)4100000011645142 035 $a(DE-He213)978-1-4842-6549-9 035 $a(MiAaPQ)EBC6421904 035 $a(CaSebORM)9781484265499 035 $a(PPN)252518586 035 $a(EXLCZ)994100000011645142 100 $a20210320d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBeginning MLOps with MLFlow $edeploy models in aws sagemaker, google cloud, and microsoft azure /$fSridhar Alla, Suman Kalyan Adari 205 $a1st ed. 2021. 210 1$aBerkeley, California :$cAPress,$d[2021] 210 4$dİ2021 215 $a1 online resource (XIV, 330 p. 267 illus.) 300 $aIncludes index. 311 $a1-4842-6548-3 327 $aChapter 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. 330 $aIntegrate 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. 606 $aComputer software 606 $aMachine learning 615 0$aComputer software. 615 0$aMachine learning. 676 $a006.31 700 $aAlla$b Sridhar$0886225 702 $aAdari$b Suman Kalyan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483568503321 996 $aBeginning MLOps with MLFlow$92854325 997 $aUNINA