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

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

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

Software -- Software Engineering.

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers

Harness the power of Apex design patterns to build robust and scalable code architectures on the Force.com platform About This Book Apply Creational, Structural and behavioural patterns in Apex to fix governor limit issues. Have a grasp of the anti patterns to be taken care in Apex which could have adverse effect on the application. The authors, Jitendra Zaa is a salesforce MVP and Anshul Verma has 12+ years of experience in the area of application development. Who This Book Is For If you are a competent developer with working knowledge of Apex, and now want to deep dive into the world of Apex design patterns to optimize the application performance, then this book is for you. Prior knowledge of Salesforce and Force.com platform is recommended. What You Will Learn Apply OOPs principal in Apex to design a robust and efficient solution to address various facets to a business problem Get to grips with the benefits and applicability of using different design patterns in Apex Solve problems while instantiating, structuring and giving dynamic behavior to Apex classes Understand the implementation of creational, structural, behavioral, concurrency and anti-patterns in your application Follow the Apex best practices to resolve governor limit issues Get clued up about the Inheritance, abstract classes, polymorphism in Apex to deal with the object mechanism Master various design patterns and determine the best out of them Explore the anti patterns that could not be applied to Apex and their appropriate solutions In Detail Apex is an on-demand programming language providing a complete set of features for building business applications – including data models and objects to manage data. Apex being a proprietor programming language from Salesforce to be worked with multi tenant environment is a lot different than traditional OOPs languages like Java and C#. It acts as a workflow engine for managing collaboration of the data between users, a user interface model to handle forms and other interactions, and a SOAP API for programmatic access and integration. Apex Design Patterns gives you an insight to several problematic situations that can arise while developing on Force.com platform and the usage of Design patterns to solve them. Packed with real life examples, it gives you a walkthrough from learning design patterns that Apex can offer us, to implementing the appropriate ones in your own application. Furthermore, we learn about the creational patterns that deal with object creation mechanism and structural patterns that helps to identify the relationship between entities. Also, the behavioural and concurrency patterns are put forward explaining the communication between objects and multi-threaded programming paradigm respectively. We later on, deal with the issues regarding structuring of classes, instantiating or how to give a dynamic behaviour at a runtime, with the help of anti-patterns. We learn the basic OOPs principal in polymorphic and modular way to enhance its capability. Also, best practices of writing Apex code are explained to differentiate between the implementation of appropriate patterns. This book will also explain some unique patterns that could be applied to get around governor limits. By the end of this book, you will be a maestro in developing your applications on Force.com for Salesforce Style and approach This book is a step-by-step guide, complete with well-tested programs and real world situations to solve your common occurring problems in Apex design by using the anti-patterns. It gets crackling from exploring every appropriate solution to comparing the best one as per OOps principal.

Experience about the design of object-oriented software, the design patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves.Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like Java1. Strategy Pattern Principle 2. Strategy Pattern Case3. Composition Pattern Principle4. Composition Pattern Case5. Singleton Pattern Principle6. Singleton Pattern Case7. Template Pattern Principle8. Template Pattern Case9. Factory Pattern Principle10. Factory Pattern Case11. Builder Pattern Principle12. Builder Pattern Case13. Adapter Pattern Principle14. Adapter Pattern Case15. Facade Pattern Principle16. Facade Pattern Case17. Decorator Pattern Principle18. Decorator Pattern Case19. Prototype Pattern Shallow Clone20. Prototype Pattern Deep Clone21. Bridge Pattern Principle22. FlyWeight Pattern Case23. Chain Pattern Principle24. Chain Pattern Case25. Command Pattern Case26. Iterator Pattern Case27. Mediator Pattern Case28. Memento Pattern Case29. Observer Pattern Case30. Visitor Pattern Case31. State Pattern Case32. Proxy Pattern Case