53 Results for : deployable

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    Motion Structures ab 52.99 € als pdf eBook: Deployable Structural Assemblies of Mechanisms. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
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    • Price: 52.99 EUR excl. shipping
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    Folding Pods ab 57.99 € als Taschenbuch: Study and small-scale design application of deployable structures. Aus dem Bereich: Bücher, Kunst & Musik,
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    • Price: 57.99 EUR excl. shipping
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    Microservices Development Cookbook ab 40.99 € als epub eBook: Design and build independently deployable modular services. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
    • Shop: hugendubel
    • Price: 40.99 EUR excl. shipping
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    Cold Hibernated Elastic Memory Structure ab 176.99 € als pdf eBook: Self-Deployable Technology and Its Applications. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
    • Shop: hugendubel
    • Price: 176.99 EUR excl. shipping
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    Cold Hibernated Elastic Memory Structure ab 177.99 € als epub eBook: Self-Deployable Technology and Its Applications. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
    • Shop: hugendubel
    • Price: 177.99 EUR excl. shipping
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    Vision and Displays for Military and Security Applications ab 179.99 € als Taschenbuch: The Advanced Deployable Day/Night Simulation Project. Auflage 2010. Aus dem Bereich: Bücher, English, International, Englische Taschenbücher,
    • Shop: hugendubel
    • Price: 179.99 EUR excl. shipping
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    Vision and Displays for Military and Security Applications ab 170.99 € als pdf eBook: The Advanced Deployable Day/Night Simulation Project. Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Technik,
    • Shop: hugendubel
    • Price: 170.99 EUR excl. shipping
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    Continuous delivery is all about making software deployments possible at any moment on demand. You achieve continuous delivery ensuring your code is always in a deployable state. By implementing continuous delivery, you are eliminating those phases which follow traditional software development practices. Some of the greatest benefits of continuous delivery include faster time to market, low-risk software releases, higher quality of products and services, significantly lower costs, and happier teams.  This may sound way too good to be true. Yet this is all true, but keep in mind that continuous delivery is not magic. You have to focus on daily improvements and constantly pursue changes and experiments. Getting software released to your users often is very challenging and time-consuming. Fortunately, you have an option to turn towards continuous delivery practices that will enable incremental delivery of high-quality functionalities to your users.  This book is going to be your best companion on this journey no matter which size your business is.  Here is a preview of what you will learn.... How to move from agile movement towards DevOps and continuous delivery The basics of continuous delivery The evolution of continuous delivery and its connection to continuous deployment The greatest continuous delivery benefits for your business The costs of implementing continuous delivery How to successfully overcome the most common continuous delivery challenges The basic test automation framework The main continuous delivery principles Turning towards continuous improvement The continuous delivery pipeline How to implement continuous delivery And much, much more! Get this audiobook now and learn more about continuous delivery! ungekürzt. Language: English. Narrator: William Bahl. Audio sample: https://samples.audible.de/bk/acx0/104487/bk_acx0_104487_sample.mp3. Digital audiobook in aax.
    • Shop: Audible
    • Price: 9.95 EUR excl. shipping
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    Tens of millions died during World War II as the warring powers raced to create the best fighter planes, tanks, and guns, and eventually that race extended to bombs which carried enough power to destroy civilization itself. While the war raged in Europe and the Pacific, a dream team of Nobel Laureates was working on the Manhattan Project, a program kept so secret that Vice President Harry Truman didn’t know about it until he took the presidency after FDR’s death in April 1945.The Manhattan Project would ultimately yield the "Little Boy" and "Fat Man" bombs that released more than 100 Terajoules of energy at Hiroshima and Nagasaki, but weeks earlier, on July 16, 1945, the first detonation of a nuclear device took place in Alamogordo, New Mexico. The first bomb was nicknamed the "gadget", to avoid espionage attempts to discover that it was, indeed, a bomb. In some sense, the device detonated in July was not really a "bomb" anyway; it was not a deployable device, though it was a detonatable one.With this success, word reached President Truman, who was then attending the Potsdam Conference, and while there, he presented the news to Soviet leader Joseph Stalin. Stalin feigned surprise; in an ironic twist of fate, espionage missions had revealed American nuclear research to the Soviets before it had even reached Vice President Truman.The attacks on Hiroshima and Nagasaki in August 1945, along with the Cold War-era tests and their accompanying mushroom clouds, would demonstrate the true power and terror of nuclear weapons, but in the late 1930s these bombs were only vaguely being thought through, particularly after the successful first experiment to split the atom by a German scientist. Despite the fact the Nazis’ quest for a nuclear weapon began in earnest in 1939, no one really had a handle on how important nuclear weapons would prove to war and geopolitics, so the Germans were hesitant to expend resources on it. Moreover, they were hampe ungekürzt. Language: English. Narrator: Gregory T. Luzitano. Audio sample: https://samples.audible.de/bk/acx0/163278/bk_acx0_163278_sample.mp3. Digital audiobook in aax.
    • Shop: Audible
    • Price: 9.95 EUR excl. shipping
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    Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists familiar with supervised machine learning and the basics of object-orientaFor data scientists who know machine learning and the basics of object-oriented programming.ed programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it's so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure
    • Shop: buecher
    • Price: 42.40 EUR excl. shipping


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