With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Von der ersten Idee bis zur konkreten Anwendung: Ihre Data-Science-Projekte in der AWS-Cloud realisieren Der US-Besteller zu Amazon Web Services jetzt auf Deutsch Beschreibt alle wichtigen Konzepte und die wichtigsten AWS-Dienste mit vielen Beispielen aus der Praxis Deckt den kompletten End-to-End-Prozess von der Entwicklung der Modelle bis zum ihrem konkreten Einsatz ab Mit Best Practices für alle Aspekte der Modellerstellung einschließlich Training, Deployment, Sicherheit und MLOps Mit diesem Buch lernen Machine-Learning- und KI-Praktiker, wie sie erfolgreich Data-Science-Projekte mit Amazon Web Services erstellen und in den produktiven Einsatz bringen. Es bietet einen detaillierten Einblick in den KI- und Machine-Learning-Stack von Amazon, der Data Science, Data Engineering und Anwendungsentwicklung vereint. Chris Fregly und Antje Barth beschreiben verständlich und umfassend, wie Sie das breite Spektrum an AWS-Tools nutzbringend für Ihre ML-Projekte einsetzen. Der praxisorientierte Leitfaden zeigt Ihnen konkret, wie Sie ML-Pipelines in der Cloud erstellen und die Ergebnisse dann innerhalb von Minuten in Anwendungen integrieren. Sie erfahren, wie Sie alle Teilschritte eines Workflows zu einer wiederverwendbaren MLOps-Pipeline bündeln, und Sie lernen zahlreiche reale Use Cases zum Beispiel aus den Bereichen Natural Language Processing, Computer Vision oder Betrugserkennung kennen. Im gesamten Buch wird zudem erläutert, wie Sie Kosten senken und die Performance Ihrer Anwendungen optimieren können.
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock
Volleyball is one of the fastest growing sports in Europe, loved for its fast pace and competitive nature, but mostly because it is great fun that can be enjoyed by virtually anyone. Aimed at those with a basic grasp of the game, "Training...Volleyball" teaches readers how to improve and optimize their skills for setting, forehand, and over ......
What is time? Is there a link between objective knowledge about time and subjective experience of time? And what is eternity? Does religion have the answer? Does science? Antje Jackelén investigates the problem and concept of time. Her analysis of the subject includes: The notion of time and eternity as it is narrated through Christian hymn books stemming from Germany, Sweden, and the English-speaking world, with insights into changes of the concept and understanding of time in Christian spirituality over the past few decades; Theological approaches to time and eternity, as well as a look at Trinitarian theology and its relation to time; The discussion of scientific theories of time, including Newtonian, relativistic, quantum, and chaos theories; The formulation of a "theology of time," a theological-mathematical model incorporating relational thinking oriented towards the future, the doctrine of trinity, and the notion of eschatology"--Descripción del editor.
In 2007, Antje Jackelén adopted the motto "God is greater" from the First Letter of John 3:18-20 for her consecration as the bishop of the Diocese of Lund. Today, as the Lutheran archbishop of Sweden, Jackelén ministers by the same, ever-expanding belief: Of all the suffering, divisiveness, and hostility in the modern world's social and political climate, God is greater. Of the human search for understanding and all the questions left unanswered, God is greater. And even of the misunderstandings--the many places where humans' understanding of God, religion, and spirituality have gone horribly wrong--God is greater. For Jackelén, the belief "God is greater" does not negate the human need to seek answers. Rather, it encourages us to seek answers that expand, instead of simplify, our own understanding. In this revised edition of her 2011 book, translated from the original Swedish, Jackelén explores an ambitious range of topics, from the interplay between religion and science to the role of faith in seemingly secular landscapes, without settling for easy answers. In a time of rising political tension, where trite answers are a dime a dozen, Jackelén proposes a path forward: If we believe that God is greater--greater than our differences, our conflicts, our best achievements, and our worst failures--then we open up incredible space for advancement. It's in this space that communities of varying beliefs and traditions can come together through both dialogue and action to find greater meaning and greater good.
The socio-political activities of the Acehnese diaspora, located mainly in Malaysia, Scandinavia, the USA and Australia, have been of fundamental importance to conflict and politics within Aceh. The intensity of the relations between the diaspora and the homeland was mainly determined by the conflict that afflicted the region between 1976 and 2005, and the resulting hardship was experienced by Acehnese both at home and abroad. This book looks at more than thirty years of long-distance politics exercised by the Acehnese diaspora both during the conflict and beyond. It interprets the social, political and cultural aspects of the small-scale conflict in Aceh, as well as focusing on the external factors related to the Acehnese overseas and their impact on homeland politics. The book goes on to contribute to the argument that the Acehnese diaspora had a significant impact on those who remained in Aceh. By focusing on the triangular relationships between the homeland, the host countries and the Acehnese diaspora, the book draws attention to the exchange of people, ideas, and financial and material resources that has occurred. It is a useful contribution to Southeast Asian Politics and Diaspora Studies.
This first in-depth study of Miranda July's work reveals some of its major motives and consequently provides fascinating insights into the lifestyle of the contemporary white Californian middle class. Through an analysis of July's award-winning intermedial work, the author lays open how July takes individualism and self-help as constitutive for the creative class. Although a member of the creative class herself, July's voice oscillates between irony and approval. July thus paints a fascinating portrait of neurotic hipsterism, which triggers self-reflection in the general reader and critical thinking in the cultural analyst.
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