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Item #:
157976159
Makine Öğrenmesi Sistemleri Tasarlamak: Üretime - Hazır Uygulamalar İçin Yinelemeli Bir Süreç
Item #:
157976159
ANG 83
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This is simply the best book you can read about how to develop, deploy, scale machine learning models in a company for the greatest impact.
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What Stands Out
Iterative Process
Emphasizes an iterative approach to machine learning system design, ensuring continuous improvement and adaptation, which leads to more refined and production-ready applications.
Practical Focus
Offers practical insights and methodologies that bridge the gap between theory and real-world application, making it invaluable for practitioners aiming to implement machine learning successfully.
Comprehensive Guidance
Provides comprehensive guidance on all phases of machine learning systems development, including planning, execution, and deployment, ensuring a well-rounded understanding for optimal results.
Product Details
- Makine öğrenmesi sistemleri hem karmaşık hem benzersizdir. Karmaşıktır çünkü farklı birçok bileşenden oluşur ve farklı birçok paydaş içerir. Benzersizdir çünkü veriye bağımlıdır ve bu veriler bir kullanım senaryosundan diğerine alabildiğine değişir. Bu kitapta güvenilir, ölçeklenebilir, sürdürülebilir ve değişen ortamlara ve iş gereksinimlerine uyabilen makine öğrenmesi sistemleri tasarlamaya yönelik bütüncül bir yaklaşım öğreneceksiniz. Claypot AI'ın kurucu ortağı olan yazar Chip Huyen, eğitim verilerinin nasıl işleneceği ve oluşturulacağı, hangi özniteliklerin kullanılacağı, modellerin hangi sıklıkla yeniden eğitileceği ve neyin izleneceği gibi her bir tasarım kararını, bu kararın sisteminizin hedeflerine bir bütün olarak ulaşmasına nasıl yardımcı olabileceği bağlamında değerlendirmektedir. Bu kitaptaki yinelemeli çerçeve, bol miktarda referans tarafından desteklenen gerçek örnek durum çalışmalarını kullanmaktadır. Bu kitap aşağıdaki gibi senaryoları ele almanıza yardımcı olacaktır: Bir iş problemini çözmek için verilerin mühendisliğini yapmak ve doğru metrikleri seçmek Modelleri sürekli olarak geliştirmek, değerlendirmek, dağıtmak ve güncellemek için süreci otomatikleştirmek Modellerinizin üretim esnasında karşılaşabileceği sorunları hızlıca tespit etmek ve ele almak için bir izleme sistemi geliştirmek Kullanım senaryoları arasında hizmet sunan bir makine öğrenmesi platformu tasarlamak Sorumlu makine öğrenmesi sistemleri geliştirmek Bu, tek kelimeyle, en büyük etki için bir şirkette makine öğrenmesi modellerinin nasıl geliştirilip, dağıtılıp, ölçekleneceğiyle ilgili okuyabileceğiniz en iyi kitaptır. Josh Wills WeaveGrid'de Yazılım Mühendisi ve Data Engineering, Slack'ın eski yöneticisi Gelişen fakat kaotik bir ekosistemde uçtan uca makine öğrenmesine yönelik bu prensipli görüş hem haritanız hem pusulanızdır: Teknoloji devlerinin içerisinde ve dışarısında bulunan pratisyenler için mutlaka okunması gereken bir kitap. Jacopo Tagliabue AI, Coveo'nun yöneticisi Daha fazla göster
| Yayıncı | Buzdağı Yayınevi |
| Yayınlanma Tarihi | 3 Mayıs 2024 |
| Baskı | 1. |
| Dil | Türkçe |
| Baskı Uzunluğu | 416 sayfa |
| ISBN-10 | 6259855230 |
| ISBN-13 | 978-6259855233 |
| Boyutlar | 16.5 x 24 x 2.5 cm |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to enhance their skills in creating production-ready machine learning systems iteratively.
-
Machine Learning Engineers
Beneficial for ML engineers needing a structured approach to deploy machine learning applications efficiently and effectively.
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AI Researchers
Useful for AI researchers focused on practical implementations and iterative processes in real-world machine learning applications.
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Beginners
Not suitable for beginners lacking foundational knowledge in machine learning concepts and systems.
Product Description
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Features & Benefits
- Learn a holistic approach to designing reliable machine learning systems.
- Make informed design decisions through real case studies.
- Automate the continuous development and updating of models.
- Develop effective monitoring systems for production problems.
- Create a flexible machine learning platform adaptable to various scenarios.
- Build responsible and sustainable machine learning systems.
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