Beginning MLOps with MLFlow: Deploy Models in AWS SageMaker, Google Cloud, and M

Description: Beginning MLOps with MLFlow by Sridhar Alla, Suman Kalyan Adari Intermediate-Advanced user level FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher 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. 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 LearnPerform 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 cloudWho This Book Is ForData scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models Back Cover 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 Author Biography Sridhar Alla is the co-founder and CTO of Bluewhale, which helps big and small organizations build AI-driven big data solutions and analytics. He is a published author of books and an avid presenter at numerous Strata, Hadoop World, Spark Summit, and other conferences. He also has several patents filed with the US PTO on large-scale computing and distributed systems. He has extensive hands-on experience in several technologies, including Spark, Flink, Hadoop, AWS, Azure, Tensorflow, Cassandra, and others. He spoke on Anomaly Detection Using Deep Learning at Strata SFO in March of 2019 and at Strata London in October of 2019. He was born in Hyderabad, India and now lives in New Jersey, USA with his wife Rosie and daughter Evelyn. When he is not busy writing code, he loves to spend time with his family and also training, coaching, and organizing meetups. Suman Kalyan Adari is an undergraduate student pursuing a BS degree in computer science atthe University of Florida. He has been conducting deep learning research in the field of cybersecurity since his freshman year, and has presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon, USA in June of 2019. He is passionate about deep learning, and specializes in its practical uses in various fields such as image recognition, anomaly detection, natural language processing, targeted adversarial attacks, and more. Table of Contents 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. Feature Covers the concepts behind MLOps that you need to know to operationalize your machine learning solutions for practical use Shows you how to deploy models with AWS SageMaker, Google Cloud, and Microsoft Azure Explains MLFlow with PyTorch, Keras, and TensorFlow Details ISBN1484265483 Author Suman Kalyan Adari Short Title Beginning MLOps with MLFlow Language English ISBN-10 1484265483 ISBN-13 9781484265482 Format Paperback Subtitle Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure DOI 10.1007/978-1-4842-6549-9 Year 2020 Pages 330 Publication Date 2020-12-08 Publisher APress Edition 1st Imprint APress Place of Publication Berkley Country of Publication United States AU Release Date 2020-12-08 NZ Release Date 2020-12-08 US Release Date 2020-12-08 UK Release Date 2020-12-08 Illustrations 267 Illustrations, black and white; XIV, 330 p. 267 illus. Edition Description 1st ed. 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