Data Engineering Design Patterns
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Machine Learning Design Patterns
Author: Valliappa Lakshmanan
language: en
Publisher: O'Reilly Media
Release Date: 2020-10-15
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Data Engineering Design Patterns
Author: Bartosz Konieczny
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2024-05-09
Data projects are an intrinsic part of an organization’s technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more. Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios. Throughout this journey, you’ll use open source data tools and public cloud services to apply each pattern. You'll learn: Challenges data engineers face and their impact on data systems How these challenges relate to data system components Useful applications of data engineering patterns How to identify and fix issues with your current data components TTechnology-agnostic solutions to new and existing data projects, with open source implementation examples Bartosz Konieczny is a freelance data engineer who's been coding since 2010. He's held various senior hands-on positions that allowed him to work on many data engineering problems in batch and stream processing.