Effective Machine Learning Teams
DOWNLOAD
Download Effective Machine Learning Teams PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Effective Machine Learning Teams book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Effective Machine Learning Teams
DOWNLOAD
Author : David Tan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-02-29
Effective Machine Learning Teams written by David Tan 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 2024-02-29 with Computers categories.
Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team's flow when building ML systems and products. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You'll learn how to overcome friction and experience flow when delivering ML solutions. You'll also learn how to: Write automated tests for ML systems, containerize development environments, and refactor problematic codebases Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions Apply Lean delivery and product practices to improve your odds of building the right product for your users Identify suitable team structures and intra- and inter-team collaboration techniques to enable fast flow, reduce cognitive load, and scale ML within your organization
Effective Machine Learning Teams
DOWNLOAD
Author : David Tan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-02-29
Effective Machine Learning Teams written by David Tan 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 2024-02-29 with Computers categories.
Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions. This book shows you how to: Apply engineering practices such as writing automated tests, containerizing development environments, and refactoring problematic code bases Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions Design maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashion Apply delivery and product practices to iteratively improve your odds of building the right product for your users Use intelligent code editor features to code more effectively.
Effective Machine Learning Teams
DOWNLOAD
Author : David Tan
language : en
Publisher: O'Reilly Media
Release Date : 2024-02-29
Effective Machine Learning Teams written by David Tan 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 2024-02-29 with categories.
Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions. This book shows you how to: Apply engineering practices such as writing automated tests, containerizing development environments, and refactoring problematic code bases Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions Design maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashion Apply delivery and product practices to iteratively improve your odds of building the right product for your users Use intelligent code editor features to code more effectively
Agile Machine Learning
DOWNLOAD
Author : Eric Carter
language : en
Publisher: Apress
Release Date : 2019-08-21
Agile Machine Learning written by Eric Carter and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-21 with Computers categories.
Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors’ approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll Learn Effectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is For Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.
Machine Learning Exam Essentials
DOWNLOAD
Author : cybellim
language : en
Publisher: Cybellium Ltd
Release Date : 2024-10-26
Machine Learning Exam Essentials written by cybellim and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Study Aids categories.
Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
The Ai Engineer S Guide To Surviving The Eu Ai Act
DOWNLOAD
Author : Larysa Visengeriyeva
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-06-27
The Ai Engineer S Guide To Surviving The Eu Ai Act written by Larysa Visengeriyeva 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 2025-06-27 with Business & Economics categories.
With the introduction of the EU AI Act, companies employing AI systems face a new set of comprehensive and stringent regulations. Dr. Larysa Visengeriyeva offers a much-needed guide for navigating these unfamiliar regulatory waters to help you meet compliance challenges with confidence. From explaining the legislative framework to sharing strategies for implementing robust MLOps and data governance practices, this wide-ranging book shows you the way to thrive, not just survive, under the EU AI Act. It's an indispensable tool for engineers, data scientists, and policymakers engaged in or planning for AI deployments within the EU. By reading, you'll gain: An in-depth understanding of the EU AI Act, including the four risk categories and what they mean for you Strategies for compliance, including practical approaches to achieving technical readiness Actionable advice on applying MLOps methodologies to ensure ongoing compliance Insights on the implications of the EU's pioneering approach to AI regulation and its global effects
Microsoft Azure Essentials Azure Machine Learning
DOWNLOAD
Author : Jeff Barnes
language : en
Publisher: Microsoft Press
Release Date : 2015-04-25
Microsoft Azure Essentials Azure Machine Learning written by Jeff Barnes and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-25 with Computers categories.
Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.
Building Intelligent Systems
DOWNLOAD
Author : Geoff Hulten
language : en
Publisher: Apress
Release Date : 2018-03-06
Building Intelligent Systems written by Geoff Hulten and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-06 with Computers categories.
Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. What You’ll Learn Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success Design an intelligent user experience: Produce data to help make the Intelligent System better over time Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice Create intelligence: Use different approaches, including machine learning Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want Who This Book Is For Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems
Data Teams
DOWNLOAD
Author : Jesse Anderson
language : en
Publisher:
Release Date : 2020
Data Teams written by Jesse Anderson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Machine Learning Engineering In Action
DOWNLOAD
Author : Ben Wilson
language : en
Publisher: Simon and Schuster
Release Date : 2022-05-17
Machine Learning Engineering In Action written by Ben Wilson 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 2022-05-17 with Computers categories.
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 who know machine learning and the basics of object-oriented 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.