Machine Learning Design Patterns
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Machine Learning Design Patterns
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Author : Valliappa Lakshmanan
language : en
Publisher: O'Reilly Media
Release Date : 2020-10-15
Machine Learning Design Patterns written by Valliappa Lakshmanan and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-15 with Computers categories.
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
Machine Learning Design Patterns
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Author : Valliappa Lakshmanan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-10-15
Machine Learning Design Patterns written by Valliappa Lakshmanan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-15 with Computers categories.
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
Machine Learning Design Patterns
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Author : Valliappa Lakshmanan
language : en
Publisher:
Release Date : 2021
Machine Learning Design Patterns written by Valliappa Lakshmanan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Machine learning categories.
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.
Deep Learning Patterns And Practices
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Author : Andrew Ferlitsch
language : en
Publisher: Simon and Schuster
Release Date : 2021-10-12
Deep Learning Patterns And Practices written by Andrew Ferlitsch and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-12 with Computers categories.
Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Assembling large-scale model deployments Optimizing hyperparameter tuning Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside Modern convolutional neural networks Design pattern for CNN architectures Models for mobile and IoT devices Large-scale model deployments Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline
Distributed Machine Learning Patterns
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Author : Yuan Tang
language : en
Publisher: Simon and Schuster
Release Date : 2024-01-30
Distributed Machine Learning Patterns written by Yuan Tang and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-30 with Computers categories.
Practical patterns for scaling machine learning from your laptop to a distributed cluster. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Build ML pipelines with data ingestion, distributed training, model serving, and more Automate ML tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade-offs between different patterns and approaches Manage and monitor machine learning workloads at scale Inside Distributed Machine Learning Patterns you’ll learn to apply established distributed systems patterns to machine learning projects—plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. About the technology Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kubeflow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. About the book Distributed Machine Learning Patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you’ll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You’ll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes. What's inside Data ingestion, distributed training, model serving, and more Automating Kubernetes and TensorFlow with Kubeflow and Argo Workflows Manage and monitor workloads at scale About the reader For data analysts and engineers familiar with the basics of machine learning, Bash, Python, and Docker. About the author Yuan Tang is a project lead of Argo and Kubeflow, maintainer of TensorFlow and XGBoost, and author of numerous open source projects. Table of Contents PART 1 BASIC CONCEPTS AND BACKGROUND 1 Introduction to distributed machine learning systems PART 2 PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS 2 Data ingestion patterns 3 Distributed training patterns 4 Model serving patterns 5 Workflow patterns 6 Operation patterns PART 3 BUILDING A DISTRIBUTED MACHINE LEARNING WORKFLOW 7 Project overview and system architecture 8 Overview of relevant technologies 9 A complete implementation
Design Patterns
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Author : Erich Gamma
language : en
Publisher: Pearson Deutschland GmbH
Release Date : 1995
Design Patterns written by Erich Gamma and has been published by Pearson Deutschland GmbH this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Business & Economics categories.
Software -- Software Engineering.
Introducing Mlops
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Author : Mark Treveil
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-11-30
Introducing Mlops written by Mark Treveil and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-30 with Computers categories.
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
Holub On Patterns
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Author : Allen Holub
language : en
Publisher: Apress
Release Date : 2004-09-27
Holub On Patterns written by Allen Holub and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-09-27 with Computers categories.
* Allen Holub is a highly regarded instructor for the University of California, Berkeley, Extension. He has taught since 1982 on various topics, including Object-Oriented Analysis and Design, Java, C++, C. Holub will use this book in his Berkeley Extension classes. * Holub is a regular presenter at the Software Development conferences and is Contributing Editor for the online magazine JavaWorld, for whom he writes the Java Toolbox. He also wrote the OO Design Process column for IBM DeveloperWorks. * This book is not time-sensitive. It is an extremely well-thought out approach to learning design patterns, with Java as the example platform, but the concepts presented are not limited to just Java programmers. This is a complement to the Addison-Wesley seminal "Design Patterns" book by the "Gang of Four".
Head First Design Patterns
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Author : Eric Freeman
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2004-10-25
Head First Design Patterns written by Eric Freeman and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-10-25 with Computers categories.
Using research in neurobiology, cognitive science and learning theory, this text loads patterns into your brain in a way that lets you put them to work immediately, makes you better at solving software design problems, and improves your ability to speak the language of patterns with others on your team.
Django Design Patterns And Best Practices
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Author : Arun Ravindran
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-31
Django Design Patterns And Best Practices written by Arun Ravindran and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-31 with Computers categories.
Learning to build more maintainable websites with Django either takes a lot of experience or familiarity with various pragmatic design patterns. This book will accelerate your journey into the world of web development. This new edition is updated with additional chapters and diagrams to help you get to grips with the current best practices in ...