Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
genuine image
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Capture best practices and solutions to recurring problems in machine learning
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Nr produktu: 30115128

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

Nr produktu: 30115128

PLN 221

Szczegóły ceny

Bez kosztów wysyłki i cła ( Koszty wysyłki i cła zostaną obliczone przy finalizacji zamówienia )

*Wszystkie produkty zostaną zaimportowane z Stany Zjednoczone

W magazynie
Stany Zjednoczone Zaimportowano ze sklepu USA
Zamów teraz i otrzymaj około Wtorek, Sierpień 25
Bezpieczna transakcjaBezpieczna transakcja

Ubuy dokłada wszelkich starań, aby chronić Twoje bezpieczeństwo i prywatność. Nasz zaawansowany system bezpieczeństwa płatności zapewnia poufność poprzez szyfrowanie Twoich danych podczas transmisji przy użyciu protokołów AES (Advanced Encryption Standards) i SSL (Secure Socket Layer). Twoje dane płatności są w 100% bezpieczne, ponieważ nie udostępniamy ich zewnętrznym sprzedawcom.

Nasi czołowi partnerzy logistyczni
  • fedex
  • dhl
Capture best practices and solutions to recurring problems in machine learning
Gwarancja U-Care:
Brak
Wybierz plan
fast shipping

Szybka
wysyłka

free return

Bezpłatny
zwrot*

Bezpieczne pakowanie

Bezpieczne pakowanie

100% oryginalne produkty

100% oryginalne produkty

pci-dss

Zgodność z PCI DSS

iso certified

Certyfikat ISO 27001

paypal payment
visa payment
mastercard payment
bank transfer payment
przelewy24 payment
blik payment
Note: Step Down Voltage Transformer required for using electronics products of Stany Zjednoczone store (110-120). Recommended power converters Kup Teraz.

Co wyróżnia ten produkt

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.

Szczegóły Produktu

Explore the 1st edition of Machine Learning Design Patterns at Ubuy Polska. Get expert solutions for data preparation, model building, and MLOps.
  • 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)

Dla kogo jest przeznaczony?

Suitable For
  • 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.

Not Suitable For
  • Absolute Beginners

    Users with no prior knowledge of machine learning might find the content too complex or advanced for their understanding.

OPIS PRODUKTU

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

Masz pytania? Porozmawiaj z nami

Pytania i odpowiedzi klientów

  • pytanie: What are Machine Learning Design Patterns?

    odpowiedź: 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.
  • pytanie: How can Machine Learning Design Patterns help in data preparation?

    odpowiedź: 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.
  • pytanie: What challenges does the book address regarding model building?

    odpowiedź: 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.
  • pytanie: How does MLOps fit into the concepts outlined in the book?

    odpowiedź: 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.
  • pytanie: Is this book suitable for beginners in machine learning?

    odpowiedź: 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.
  • pytanie: What are some practical use cases for the design patterns presented in this book?

    odpowiedź: 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.
  • pytanie: How frequently should one refer to design patterns when developing models?

    odpowiedź: 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.
  • pytanie: Can you explain the relationship between data preparation and model performance?

    odpowiedź: 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.
  • pytanie: What reader background is assumed for this book?

    odpowiedź: 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.
  • pytanie: Where can I buy Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 1st Edition in Poland?

    odpowiedź: 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 Poland. 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.

Opinie i oceny klientów

1 oceny klientów
  • 5 gwiazdka
    100%
  • 4 gwiazdka
    0%
  • 3 gwiazdka
    0%
  • 2 gwiazdka
    0%
  • 1 gwiazdka
    0%

Zrecenzuj ten produkt

Podziel się opinią z innymi klientami

Zalety

  • 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

Wady

  • May be too basic for experienced ML researchers/engineers

Zaufanie do platformy i bezpieczeństwo kupujących

trustpilot logo
4.2/5 9250 + reviews
Przeczytaj opinie
MA
Mohammad
Zweryfikowany kupujący

“Excellent quality and original too,when ubuy send original things I will appreciate that ,I very satisfied thank you”

23 July 2026 · via Trustpilot
CK
Chitra
Zweryfikowany kupujący

“The order and delivery progress was communicated very well. Package arrived within the estimated time. Products arrived as expected in good condition.”

22-Jul-26 · via Trustpilot
K
kund
Zweryfikowany kupujący

“Good supply of products. Safe payment methods, and shipment worldwide! Genuine products.”

23-Jul-26 · via Trustpilot
OO
Onesi
Zweryfikowany kupujący

“Was my first time buying a product from Ubuy, but I found it so helpful. This is reliable. Gonna place a new order!”

22 July 2026 · via Trustpilot
SJ
SAO JOHN
Zweryfikowany kupujący

“Ubuy is a great online platform to buy stuff. Reliable, fast delivery time and great value for money. I've been using Ubuy online shopping platform for three years now and will continue to do so.”

23 July 2026 · via Trustpilot
Bezpieczna realizacja zamówienia Dostawa na cały świat Łatwe zwroty Oryginalne produkty

Historia ceny produktu

Ważne informacje

  • Ograniczenia: W przypadku produktów wysyłanych za granicę należy pamiętać, że wszelkie gwarancje producenta mogą być nieważne; opcje serwisowe producenta mogą być niedostępne; instrukcje obsługi produktów, podręczniki i ostrzeżenia dotyczące bezpieczeństwa mogą nie być dostępne w języku kraju docelowego; produkty (i materiały towarzyszące) mogą nie spełniać norm wykonania, specyfikacji i wymogów dotyczących oznaczeń kraju docelowego; produkty mogą nie spełniać standardów dotyczących napięcia i innych norm elektrycznych kraju docelowego (co w stosownych przypadkach wymaga użycia adaptera lub konwertera). Odbiorca jest odpowiedzialny za sprawdzenie, czy produkt może zostać legalnie zaimportowany do kraju docelowego. W przypadku zamawiania produktów w sklepie Ubuy lub u jego partnerów odbiorca jest oficjalnie zgłoszonym importerem i musi spełniać wszystkie wymogi przepisów oraz regulacji kraju docelowego.
  • Nie wszystkie produkty znajdujące się na Ubuy są na sprzedaż, ponieważ Ubuy to globalna wyszukiwarka. Produkty podlegają przepisom eksportowym/handlowym.

PLN 221

Zamów teraz i otrzymaj około Wtorek, Sierpień 25

Ilość:

Bezpieczna transakcjaBezpieczna transakcja

Zgodność z PCI DSS oraz certyfikat ISO 27001:2022, szyfrowane płatności i pełna ochrona kupującego przy każdym zamówieniu.

Nasi czołowi partnerzy logistyczni
  • fedex
  • dhl
Najszybsza dostawa międzynarodowa

Cechy i zalety

  • 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

Gwarancja Ubuy

Ciesz się bezstresowymi zakupami dzięki 100% oryginalnym produktom, bezpieczeństwu płatności zgodnemu z PCI DSS, ochronie danych z certyfikatem ISO 27001, najszybszej dostawie międzynarodowej, bezpłatnym zwrotom* oraz bezpiecznemu pakowaniu przy każdym zamówieniu.