- الصفحة الرئيسية /
- الكتب /
- الكمبيوتر والتكنولوجيا /
- علوم الكمبيوتر /
- AI & Machine Learning /
- Intelligence & Semantics /
- Machine Learning Design Patterns: Solutions t...
0 ratings
منتج #:
30115128
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
88% من المشترين سيوصون بهذا المنتج لصديق
منتج #:
30115128
EGP 2768
تفاصيل السعر
باستثناء رسوم الشحن والجمارك ( سيتم احتساب رسوم الشحن والجمارك عند إتمام الشراء )
*سيتم استيراد جميع العناصر من أمريكا
41%
متوفر فى المخزون
مستورد من متجر USA
كمية:
بقي 4 فقط من المنتج في المخزن.
اطلب الآن واحصل عليه حول الجمعة, أكتوبر 09
معاملة آمنة
تعمل يوباي جاهدة لحماية أمنك وخصوصيتك. يضمن نظام أمان الدفع المتقدم لدينا السرية من خلال تشفير معلوماتك أثناء النقل باستخدام بروتوكولات AES (معايير التشفير المتقدمة) وSSL (طبقة المنافذ الآمنة). تفاصيل الدفع الخاصة بك آمنة بنسبة %100 لأننا لا نشارك تفاصيل الدفع الخاصة بك مع بائعين تابعين لجهات خارجية
أفضل شركائنا اللوجستيين
Capture best practices and solutions to recurring problems in machine learning
شحن
سريع
استرجاع
مجاني*
تغليف آمن
منتجات أصلية %100
الامتثال لمعيار PCI DSS
حاصل على شهادة ISO 27001
Note: Step Down Voltage Transformer required for using electronics products of أمريكا store (110-120). Recommended power converters اشتري الآن.
أبرز ما يلفت الانتباه
Comprehensive Patterns
Presents a wide array of design patterns that address common machine learning challenges, from data preparation to deployment, offering practical solutions backed by real-world examples.
Expert Insights
Authored by industry experts, this book provides deep insights into best practices, helping practitioners and data scientists efficiently navigate complex problems in model building and MLOps.
Hands-On Approach
Incorporates tangible case studies and actionable strategies, enabling users to apply theoretical concepts directly to their projects, maximizing learning and effectiveness in machine learning applications.
تفاصيل المنتج
- Catalog of machine learning design patterns capturing best practices and solutions to recurring problems in machine learning
- Written by three Google engineers, offering 30 design patterns for data representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness
- Each pattern includes problem description, potential solutions, and recommendations for choosing the best technique
- Addresses challenges in training, evaluating, and deploying ML models, data representation, model type selection, robust training loop construction, scalable deployment, and model prediction interpretation
- Targets data scientists, data engineers, and ML engineers with prior knowledge of machine learning and data processing
- Excludes in-depth coverage of ML algorithms, building blocks, model architectures, model layers, and custom training loops, focusing on common enterprise machine learning patterns
| Publisher | O'Reilly Media |
| Publication date | November 24, 2020 |
| Edition | 1st |
| Language | English |
| Print length | 405 pages |
| ISBN-10 | 1098115783 |
| ISBN-13 | 978-1098115784 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 9.06 x 0.94 x 6.85 inches (23 x 2.4 x 17.4 cm) |
من يجب أن يشتري؟
-
Data Scientists
Ideal for data scientists seeking practical solutions to streamline their workflows in model building and data preparation.
-
ML Engineers
ML engineers can benefit from structured approaches to implement MLOps principles effectively while addressing deployment challenges.
-
Students and Practitioners
Students of machine learning will find this resource invaluable for understanding common industry challenges and solutions.
-
Absolute Beginners
Users with no prior knowledge of machine learning might find the content too complex or advanced for their understanding.
وصف المنتج
هل لديك أي استفسار؟ تحدث معنا
أسئلة العملاء & الإجابات
-
سؤال:
What are Machine Learning Design Patterns?
إجابه: Machine Learning Design Patterns are proven solutions to common challenges encountered in machine learning workflows. These patterns offer frameworks for data preparation, model building, and MLOps to streamline processes and improve outcomes. Understanding these patterns is essential for practitioners because they provide insights into best practices, making it easier to tackle complex projects. For instance, a developer might apply a specific pattern when dealing with missing data to enhance model accuracy, potentially saving considerable time and resources. -
سؤال:
How can Machine Learning Design Patterns help in data preparation?
إجابه: Machine Learning Design Patterns significantly aid in data preparation by offering methodologies to handle data quality issues, feature selection, and transformation processes. By utilizing these patterns, data scientists can efficiently clean, preprocess, and structure their datasets, ensuring that models are trained on the best possible data. For example, applying a pattern for feature engineering allows for the creation of more informative attributes, ultimately leading to improved model performance in prediction tasks. -
سؤال:
What challenges does the book address regarding model building?
إجابه: The book addresses various challenges in model building, such as overfitting, underfitting, and model selection. It provides patterns that guide practitioners on how to balance model complexity and performance. By using established patterns, one can determine the right balance between bias and variance to enhance model robustness. For instance, employing cross-validation techniques as detailed in the book can help in selecting models that generalize better on unseen data. -
سؤال:
How does MLOps fit into the concepts outlined in the book?
إجابه: MLOps is an integral part of the concepts outlined in the book, as it focuses on operationalizing machine learning models. The book offers design patterns that facilitate collaboration between data science and IT operations while ensuring that models can be deployed, monitored, and maintained effectively. For instance, implementing a model deployment pattern can streamline the process of moving from development to production, promoting a faster feedback loop and better scalability. -
سؤال:
Is this book suitable for beginners in machine learning?
إجابه: Yes, 'Machine Learning Design Patterns' is suitable for beginners, as it not only introduces key concepts but also provides actionable insights and examples. The book is structured to be approachable, breaking down complex ideas into understandable components. Beginners can benefit from the hands-on approaches and scenarios that illustrate how to implement these patterns in real-world projects, making the learning curve more manageable. -
سؤال:
What are some practical use cases for the design patterns presented in this book?
إجابه: The design patterns presented in the book can be applied to various practical use cases, including fraud detection, recommendation systems, and image classification. For instance, a retail company might utilize a recommendation system pattern to enhance user experience by suggesting products based on past purchase behavior. Similarly, in healthcare, patterns can be employed to predict patient outcomes by analyzing historical data, leading to better decision-making. -
سؤال:
How frequently should one refer to design patterns when developing models?
إجابه: Referring to design patterns should occur frequently throughout the model development lifecycle. These patterns serve as quick references during data preparation, feature engineering, and model evaluation stages. Practitioners can leverage patterns when encountering specific issues or when faced with decisions regarding model architecture. By continuously consulting them, data professionals can ensure that they adhere to industry best practices and are mindful of common pitfalls. -
سؤال:
Can you explain the relationship between data preparation and model performance?
إجابه: Data preparation is crucially linked to model performance, as the quality and structure of data directly influence how well a model learns. The design patterns in this book emphasize various techniques for data cleaning, transformation, and augmentation, which can significantly enhance model accuracy. For example, a well-prepared dataset where irrelevant features are eliminated and relevant features are engineered can lead to marked improvements in a model's predictive capabilities. -
سؤال:
What reader background is assumed for this book?
إجابه: The book assumes that readers have a foundational understanding of machine learning concepts and are familiar with programming, particularly in Python. However, it progresses from basic to more advanced topics, making it suitable for both novice and experienced practitioners. Readers with knowledge of statistical techniques, data analysis, and some coding experience will find the patterns presented to be relevant and applicable to their work. -
سؤال:
Where can I buy Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 1st Edition in Egypt?
إجابه: You can purchase 'Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 1st Edition' from Ubuy, which offers a wide selection of books and ensures reliable service for customers in Egypt. Ubuy provides an excellent platform where you can find this title along with various other resources related to machine learning and data science.
Intelligence & Semantics Editorial Review
"Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps" is a book that aims to provide practical solutions to common challenges in the field of machine learning. The book covers design patterns for data treatment, model design, and MLOps, with a focus on computer science perspectives. Overall, the book received positive reviews from readers. Many appreciated the easy-to-read structure and the abundance of real-world examples. It was reassuring for readers to see patterns they use in practice documented in the book. The book was also praised for providing alternative design patterns that were not previously known. However, some reviewers noted potential limitations in the book. The content seemed to only scratch the surface of machine learning practice, making it more suitable for beginners or laymen. On the other hand, the omission of technical details made it difficult for those unfamiliar with the described approaches to understand how they work. Additionally, some felt that the book overly focused on promoting technologies related to Google Cloud and Tensorflow, rather than discussing ideas in a technology-agnostic manner. Despite these limitations, the book was generally recommended for its value in providing an understanding of the toolkit that machine learning engineers need for model development. Readers found Chapter 8 particularly useful, as it delved into common patterns by use case and data type, enumerating different types of problems and the tools to tackle them.
مراجعات العملاء وتقييماتهم
418 تقييمات العملاء
-
5 نجمة
76%
-
4 نجمة
17%
-
3 نجمة
5%
-
2 نجمة
2%
-
1 نجمة
0%
أضف تقييم لهذا المنتج
شارك أفكارك مع عملاء آخرين
إيجابيات
- Provides practical solutions and alternative design patterns
- Easy-to-read structure with real-world examples
- Valuable reference for specific machine learning workflows
- Covers common challenges in data preparation, model building, and MLOps
سلبيات
- May be too basic for experienced ML researchers/engineers
منصة موثوقة وثقة كاملة للمشتري
JK
Jasmin
مشتري موثّق
“Great products and very good service: very easy and very fast international delivery.”
9 September 2026 · عبر Trustpilot
AG
Anke
مشتري موثّق
“Wonderful online shopping experience, smooth transaction from the start. Payment method works conveniently and delivery is unexpectedly fast and reliable. You go the extra mile for service. What makes this even more amazing, you deliver to Namibia. I will remain a happy Ubuy customer and will increase my purchases for sure! Thank you!”
10 September 2026 · عبر Trustpilot
H
Hazel
مشتري موثّق
“Very easy to find the products what you need, and so fast delivery, that’s why I highly recommended to others costumers to used ubuy.”
10 September 2026 · عبر Trustpilot
O
Opaleye
مشتري موثّق
“I received exactly what I ordered I was skeptical about your site because that was my first time to order. But the order came timely and neatly packaged. I was not disappointed. Thank you.”
11 September 2026 · عبر Trustpilot
AC
Adele
مشتري موثّق
“Easy to find and order what you want on the website. Delivery is quick to the UK”
8 September 2026 · عبر Trustpilot
الخروج الآمن
شحن عالمي
إسترجاع سهل
منتجات أصلية
تاريخ سعر المنتج
معلومات مهمة
- القيود: بالنسبة للمنتجات التي يتم شحنها دولياً، يُرجى ملاحظة أن أي ضمان من الشركة المصنعة قد لا يكون صالحاً؛ قد لا تتوفر خيارات خدمة الشركة المصنعة؛ قد لا تكون أدلة المنتج والتعليمات وتحذيرات السلامة مكتوبة بلغة بلد المقصد؛ قد لا يتم تصميم المنتجات (والمواد المصاحبة لها) وفقاً لمعايير بلد الوجهة والمواصفات ومتطلبات الملصقات؛ وقد لا تتوافق المنتجات مع الجهد الكهربي المستخدم في بلد الوجهة والمعايير الكهربائية الأخرى (تتطلب استخدام محوّل كهربي أو جهاز تحويل إذا كان ذلك مناسباً). المستلم مسؤول عن ضمان إمكانية استيراد المنتج بشكل قانوني إلى بلد الوجهة. عند الطلب من يوباي أو الشركات التابعة لها، يكون المستلم هو المستورد المسجل ويجب أن يلتزم بجميع القوانين واللوائح الخاصة ببلد الوجهة.
- ليست كل المنتجات المدرجة على يوباي معروضة للبيع، لأن يوباي هو محرك بحث عالمي. المنتجات تخضع للوائح التصدير / التجارة.
EGP 2768
اطلب الآن واحصل عليه حول الجمعة, أكتوبر 09
هذا المنتج غير ممنوع في بلدي. (الرجاء الضغط على الرابط أعلاه إذا لم يكن هذا المنتج ممنوعاً في بلدك ، لذلك سيقوم فريقنا بمراجعته والسماح به.)
كمية:
بقي 4 فقط من المنتج في المخزن.
معاملة آمنة
نوفر لك مدفوعات مشفّرة، وحماية متكاملة للمشتري، مع الالتزام بمعايير PCI DSS وشهادة ISO 27001:2022 لضمان أعلى مستويات الأمان في كل عملية شراء.
أفضل شركائنا اللوجستيين
أسرع توصيل عبر الحدود
المميزات والفوائد
- 30 design patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness
- Identify and mitigate common challenges in training, evaluating, and deploying ML models
- Represent data for different ML model types
- Choose the right model type for specific problems
- Build a robust training loop and deploy scalable ML systems
- Interpret model predictions and ensure fairness for users
ضمان Ubuy
تسوّق بثقة مع منتجات أصلية %100، ومدفوعات آمنة متوافقة مع معيار PCI DSS، وحماية بيانات معتمدة وفق ISO 27001، وشحن دولي سريع، وإرجاع مجاني*، وتغليف آمن لكل طلب.