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The Lean Approach to Digital Transformation (eBook, ePUB)

The Lean Approach to Digital Transformation: From Customer to Code and From Code to Customer is organized into three parts that expose and develop the three capabilities that are essential for a successful digital transformation: 1. Understanding how to co-create digital services with users, whether they are customers or future customers. This ability combines observation, dialogue, and iterative experimentation. The approach proposed in this book is based on the Lean Startup approach, according to an extended vision that combines Design Thinking and Growth Hacking. Companies must become truly "customer-centric", from observation and listening to co-development. The revolution of the digital age of the 21st century is that customer orientation is more imperative -- the era of abundance, usages rate of change, complexity of experiences, and shift of power towards communities -- are easier, using digital tools and digital communities. 2. Developing an information system (IS) that is the backbone of the digital transformation - called "exponential information system" to designate an open IS (in particular on its borders), capable of interfacing and combining with external services, positioned as a player in software ecosystems and built for processing scalable and dynamic data flows. The exponential information system is constantly changing and it continuously absorbs the best of information processing technology, such as Artificial Intelligence and Machine Learning. 3. Building software "micro-factories" that produce service platforms, which are called "Lean software factories." This "software factory" concept covers the integration of agile methods, tooling and continuous integration and deployment practices, a customer-oriented product approach, and a platform approach based on modularity, as well as API-based architecture and openness to external stakeholders. This software micro-factory is the foundation that continuously produces and provides constantly evolving services. These three capabilities are not unique or specific to this book, they are linked to other concepts such as agile methods, product development according to lean principles, software production approaches such as CICD (continuous integration and deployment) or DevOps. This book weaves a common frame of reference for all these approaches to derive more value from the digital transformation and to facilitate its implementation. The title of the book refers to the "lean approach to digital transformation" because the two underlying frameworks, Lean Startup and Lean Software Factory, are directly inspired by Lean, in the sense of the Toyota Way. The Lean approach is present from the beginning to the end of this book -- it provides the framework for customer orientation and the love of a job well done, which are the conditions for the success of a digital transformation.

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Price: 26.95 EUR excl. shipping

The Lean Approach to Digital Transformation (eBook, PDF)

The Lean Approach to Digital Transformation: From Customer to Code and From Code to Customer is organized into three parts that expose and develop the three capabilities that are essential for a successful digital transformation: 1. Understanding how to co-create digital services with users, whether they are customers or future customers. This ability combines observation, dialogue, and iterative experimentation. The approach proposed in this book is based on the Lean Startup approach, according to an extended vision that combines Design Thinking and Growth Hacking. Companies must become truly "customer-centric", from observation and listening to co-development. The revolution of the digital age of the 21st century is that customer orientation is more imperative -- the era of abundance, usages rate of change, complexity of experiences, and shift of power towards communities -- are easier, using digital tools and digital communities. 2. Developing an information system (IS) that is the backbone of the digital transformation - called "exponential information system" to designate an open IS (in particular on its borders), capable of interfacing and combining with external services, positioned as a player in software ecosystems and built for processing scalable and dynamic data flows. The exponential information system is constantly changing and it continuously absorbs the best of information processing technology, such as Artificial Intelligence and Machine Learning. 3. Building software "micro-factories" that produce service platforms, which are called "Lean software factories." This "software factory" concept covers the integration of agile methods, tooling and continuous integration and deployment practices, a customer-oriented product approach, and a platform approach based on modularity, as well as API-based architecture and openness to external stakeholders. This software micro-factory is the foundation that continuously produces and provides constantly evolving services. These three capabilities are not unique or specific to this book, they are linked to other concepts such as agile methods, product development according to lean principles, software production approaches such as CICD (continuous integration and deployment) or DevOps. This book weaves a common frame of reference for all these approaches to derive more value from the digital transformation and to facilitate its implementation. The title of the book refers to the "lean approach to digital transformation" because the two underlying frameworks, Lean Startup and Lean Software Factory, are directly inspired by Lean, in the sense of the Toyota Way. The Lean approach is present from the beginning to the end of this book -- it provides the framework for customer orientation and the love of a job well done, which are the conditions for the success of a digital transformation.

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Price: 26.95 EUR excl. shipping

Machine Learning Engineering in Action (eBook, ePUB)

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists familiar with supervised machine learning and the basics of object-orientaFor data scientists who know machine learning and the basics of object-oriented programming.ed programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it's so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure

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Price: 42.40 EUR excl. shipping

Machine Learning Engineering in Action

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists familiar with supervised machine learning and the basics of object-orientaFor data scientists who know machine learning and the basics of object-oriented programming.ed programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it's so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure

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Price: 59.99 EUR excl. shipping

Perl Best Practices (eBook, ePUB)

Many programmers code by instinct, relying on convenient habits or a &quote;style&quote; they picked up early on. They aren't conscious of all the choices they make, like how they format their source, the names they use for variables, or the kinds of loops they use. They're focused entirely on problems they're solving, solutions they're creating, and algorithms they're implementing. So they write code in the way that seems natural, that happens intuitively, and that feels good.But if you're serious about your profession, intuition isn't enough. Perl Best Practices author Damian Conway explains that rules, conventions, standards, and practices not only help programmers communicate and coordinate with one another, they also provide a reliable framework for thinking about problems, and a common language for expressing solutions. This is especially critical in Perl, because the language is designed to offer many ways to accomplish the same task, and consequently it supports many incompatible dialects.With a good dose of Aussie humor, Dr. Conway (familiar to many in the Perl community) offers 256 guidelines on the art of coding to help you write better Perl code--in fact, the best Perl code you possibly can. The guidelines cover code layout, naming conventions, choice of data and control structures, program decomposition, interface design and implementation, modularity, object orientation, error handling, testing, and debugging.They're designed to work together to produce code that is clear, robust, efficient, maintainable, and concise, but Dr. Conway doesn't pretend that this is the one true universal and unequivocal set of best practices. Instead, Perl Best Practices offers coherent and widely applicable suggestions based on real-world experience of how code is actually written, rather than on someone's ivory-tower theories on howsoftware ought to be created.Most of all, Perl Best Practices offers guidelines that actually work, and that many developers around the world are already using. Much like Perl itself, these guidelines are about helping you to get your job done, without getting in the way.Praise for Perl Best Practices from Perl community members:&quote;As a manager of a large Perl project, I'd ensure that every member of my team has a copy of Perl Best Practices on their desk, and use it as the basis for an in-house style guide.&quote;-- Randal Schwartz&quote;There are no more excuses for writing bad Perl programs. All levels of Perl programmer will be more productive after reading this book.&quote;-- Peter Scott&quote;Perl Best Practices will be the next big important book in the evolution of Perl. The ideas and practices Damian lays down will help bring Perl out from under the embarrassing heading of &quote;scripting languages&quote;. Many of us have known Perl is a real programming language, worthy of all the tasks normally delegated to Java and C++. With Perl Best Practices, Damian shows specifically how and why, so everyone else can see, too.&quote;-- Andy Lester&quote;Damian's done what many thought impossible: show how to build large, maintainable Perl applications, while still letting Perl be the powerful, expressive language that programmers have loved for years.&quote;-- Bill Odom&quote;Finally, a means to bring lasting order to the process and product of real Perl development teams.&quote;-- Andrew Sundstrom&quote;Perl Best Practices provides a valuable education in how to write robust, maintainable Perl, and is a definitive citation source when coaching other programmers.&quote;-- Bennett Todd&quote;I've been teaching Perl for years, and find the same question keeps being asked: Where can I find a reference for writing reusable, maintainable Perl code? Finally I have a decent answer.&quote;-- Paul Fenwick&quote;At last a well researched, well thought-out, comprehensive guide to Perl style. Instead of each of us developing our own, we can learn good practices from one of Perl's most prolific and experienced authors. I recommend this book to anyone who prefers getting on with the job rather than going back and fixing errors caused by syntax and poor style issues.&quote;-- Jacinta Richardson&quote;If you care about programming in any language read this book. Even if you don't intend to follow all of the practices, thinking through your style will improve it.&quote;-- Steven Lembark&quote;The Perl community's best author is back with another outstanding book. There has never been a comprehensive reference on high quality Perl coding and style until Perl Best Practices. This book fills a large gap in every Perl bookshelf.&quote;-- Uri Guttman

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Price: 19.95 EUR excl. shipping

Conditionals and Modularity in General Logics

Conditionals and Modularity in General Logics - Auflage 2011: ab 139.99 €

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Price: 139.99 EUR excl. shipping

Chomsky's Modularity Hypothesis - Is There an Innate Language Module?

Chomsky's Modularity Hypothesis - Is There an Innate Language Module? - 1. Auflage: ab 12.99 €

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Price: 12.99 EUR excl. shipping

Williams Syndrome in Psycholinguistics

Williams Syndrome in Psycholinguistics - A single developmental disorder as evidence for the modularity of the language faculty?. 1. Auflage: ab 12.99 €

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Price: 12.99 EUR excl. shipping