Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The Defense Materials Manufacturing and Infrastructure standing and planning committee of the National Academies of Sciences, Engineering, and Medicine convened a workshop on May 19-20, 2016, to discuss the value of collaboration between the materials and civil engineering communities in addressing the following problem: People and equipment are continuously monitored through multiple organizations and instruments, but the physical infrastructure where they live, train, and deploy receives little attention until it fails or is shown to be inadequate. The workshop was organized into three sessions: (1) highway infrastructure, (2) waterways infrastructure, and (3) railways infrastructure. Within these three sessions, individual speakers gave presentations on technical, functional, and economic paradigms and answered questions from workshop participants. Following these sessions, a panel discussion was held to discuss existing gaps as well as ways to overcome challenges. This publication summarizes the presentations and discussion of the workshop.

Service life (Engineering) --- Predictive analytics. --- Predictive analytics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The connected world offers the potential for radical new business insights gleaned from previously unimaginable volumes of data. But business has got bogged down in the process of collecting and storing that data; money has been wasted on data lakes in which many IT departments have drowned without being able to deliver useful insights to business leaders. Big data has new and exciting answers to offer, but business leaders must first decide what questions it would like to see answered. Data may be the new oil, but to date we have only built oil depots. This book analyses the new, Fourth Wave of business transformation, which will build the refineries that turn data into useful products. Business has started from 'data up' and needs to start again from 'value down', going back to the drivers of real business value and deciding what insights would help realize that value. Only then can we begin to interrogate data with purpose.

Artificial intelligence --- Predictive analytics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Uwe Seebacher's 'Predictive Intelligence für Manager' explores the integration of predictive intelligence into business management. It provides insights into developing technologies and strategies to enhance data-driven decision-making. By detailing the evolution of industrial management and the application of predictive models, the book aims to equip managers with tools for optimizing business processes and strategic planning. The author shares case studies and personal experiences to illustrate the impact of predictive intelligence on organizational efficiency and innovation. The intended audience includes managers and business professionals seeking to leverage data analytics for improved outcomes.

Predictive analytics. --- Business intelligence. --- Predictive analytics --- Business intelligence

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book, 'Data Analytics with Google Cloud Platform' by Murari Ramuka, provides an in-depth guide to building real-time data analytics solutions using the Google Cloud Platform (GCP). Aimed at data analytics professionals and developers, it covers the setup of end-to-end data pipelines and the implementation of data services, including BigQuery, Cloud Dataflow, and machine learning APIs. The author, with over 12 years of experience in data analytics, leverages his expertise to present practical examples and industry use cases. The book also offers insights into cloud computing benefits, architecture, and the application of machine learning models. It is designed to help readers understand and apply GCP's powerful capabilities to solve real-world business problems.

Cloud computing. --- Predictive analytics. --- Cloud computing --- Predictive analytics