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Build predictive models using large data volumes and deploy them to production using cutting-edge techniques Key Features Build highly accurate state-of-the-art machine learning models against large-scale data Deploy models for batch, real-time, and streaming data in a wide variety of target production systems Explore all the new features of the H2O AI Cloud end-to-end machine learning platform Book Description H2O is an open source, fast, and scalable machine learning framework that allows you to build models using big data and then easily productionalize them in diverse enterprise environments. Machine Learning at Scale with H2O begins with an overview of the challenges faced in building machine learning models on large enterprise systems, and then addresses how H2O helps you to overcome them. You'll start by exploring H2O's in-memory distributed architecture and find out how it enables you to build highly accurate and explainable models on massive datasets using your favorite ML algorithms, language, and IDE. You'll also get to grips with the seamless integration of H2O model building and deployment with Spark using H2O Sparkling Water. You'll then learn how to easily deploy models with H2O MOJO. Next, the book shows you how H2O Enterprise Steam handles admin configurations and user management, and then helps you to identify different stakeholder perspectives that a data scientist must understand in order to succeed in an enterprise setting. Finally, you'll be introduced to the H2O AI Cloud platform and explore the entire machine learning life cycle using multiple advanced AI capabilities. By the end of this book, you'll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs. What you will learn Build and deploy machine learning models using H2O Explore advanced model-building techniques Integrate Spark and H2O code using H2O Sparkling Water Launch self-service model building environments Deploy H2O models in a variety of target systems and scoring contexts Expand your machine learning capabilities on the H2O AI Cloud Who this book is for This book is for data scientists and machine learning engineers who want to gain hands-on machine learning experience by building and deploying state-of-the-art models with advanced techniques using H2O technology. An understanding of the data science process and experience in Python programming is recommended. This book will also benefit students by helping them understand how machine learning works in real-world enterprise scenarios.

Machine learning. --- Predictive analytics.

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Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O.ai Key Features Learn how to train the best models with a single click using H2O AutoML Get a simple explanation of model performance using H2O Explainability Easily deploy your trained models to production using H2O MOJO and POJO Book Description With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in - it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities. You'll begin by understanding how H2O's AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you'll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you'll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you'll take a hands-on approach to implementation using H2O that'll enable you to set up your ML systems in no time. By the end of this H2O book, you'll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science. What you will learn Get to grips with H2O AutoML and learn how to use it Explore the H2O Flow Web UI Understand how H2O AutoML trains the best models and automates hyperparameter optimization Find out how H2O Explainability helps understand model performance Explore H2O integration with scikit-learn, the Spring Framework, and Apache Storm Discover how to use H2O with Spark using H2O Sparkling Water Who this book is for This book is for engineers and data scientists who want to quickly adopt machine learning into their products without worrying about the internal intricacies of training ML models. If you're someone who wants to incorporate machine learning into your software system but don't know where to start or don't have much expertise in the domain of ML, then you'll find this book useful. Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python will be helpful.

Machine learning. --- Predictive analytics.

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This thesis by Mike Gerdes explores predictive health monitoring systems for aircraft using decision trees. It addresses the significant costs associated with unscheduled aircraft maintenance and proposes methods to forecast potential failures, enhancing efficiency for operators. The work integrates system monitoring, time series forecasting, and a combined approach to create a comprehensive monitoring process. Decision trees, optimized through genetic algorithms, are used to improve predictive accuracy and allow for human adjustments. The research is aimed at reducing maintenance-related delays and costs, primarily targeting aviation industry professionals and researchers in predictive maintenance technologies.

Predictive analytics. --- Decision trees.

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This text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.

Predictive analytics. --- Analytics, Predictive --- Quantitative research

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Big data. --- Machine learning. --- Predictive analytics.