Managing Machine Learning Projects
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Managing Machine Learning Projects
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Author : Simon Thompson
language : en
Publisher: Simon and Schuster
Release Date : 2023-07-25
Managing Machine Learning Projects written by Simon Thompson 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 2023-07-25 with Computers categories.
Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required! In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including: Understanding an ML project’s requirements Setting up the infrastructure for the project and resourcing a team Working with clients and other stakeholders Dealing with data resources and bringing them into the project for use Handling the lifecycle of models in the project Managing the application of ML algorithms Evaluating the performance of algorithms and models Making decisions about which models to adopt for delivery Taking models through development and testing Integrating models with production systems to create effective applications Steps and behaviors for managing the ethical implications of ML technology Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You’ll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book’s strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues. About the Technology Ferrying machine learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you’ll need to ensure your projects succeed. About the Book Managing Machine Learning Projects is an amazing source of battle-tested techniques for effective delivery of real-life machine learning solutions. The book is laid out across a series of sprints that take you from a project proposal all the way to deployment into production. You’ll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real value—read this book to make sure your project is a success. What's Inside Set up infrastructure and resource a team Bring data resources into a project Accurately estimate time and effort Evaluate which models to adopt for delivery Integrate models into effective applications About the Reader For anyone interested in better management of machine learning projects. No technical skills required. About the Author Simon Thompson has spent 25 years developing AI systems to create applications for use in telecoms, customer service, manufacturing and capital markets. He led the AI research program at BT Labs in the UK, and is now the Head of Data Science at GFT Technologies. Table of Contents 1 Introduction: Delivering machine learning projects is hard; let’s do it better 2 Pre-project: From opportunity to requirements 3 Pre-project: From requirements to proposal 4 Getting started 5 Diving into the problem 6 EDA, ethics, and baseline evaluations 7 Making useful models with ML 8 Testing and selection 9 Sprint 3: system building and production 10 Post project (sprint O)
Introducing Hr Analytics With Machine Learning
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Author : Christopher M. Rosett
language : en
Publisher: Springer Nature
Release Date : 2021-06-14
Introducing Hr Analytics With Machine Learning written by Christopher M. Rosett and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-14 with Psychology categories.
This book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. In order for today’s organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. Similarly, today’s data scientists must appreciate the unique aspects of behavioral data and the special circumstances which surround HR data and HR systems. Finally, traditional HR professionals must become familiar with research methods, statistics, and data systems in order to collaborate with these new specialized partners and teams. Despite the increasing importance of this diversity of skill, many organizations are still unprepared to build teams with the comprehensive skills necessary to have high performing HR Analytics functions. And importantly, all these considerations are magnified by the introduction and acceleration of machine learning in HR. This book will serve as an introduction to these areas and provide guidance on building the connectivity across domains required to establish well-rounded skills for individuals and best practices for organizations when beginning to apply advanced analytics to workforce data. It will also introduce machine learning and where it fits within the larger HR Analytics framework by explaining many of its basic tenets and methodologies. By the end of the book, readers will understand the skills required to do advanced HR analytics well, as well as how to begin designing and applying machine learning within a larger human capital strategy.
Machine Learning And Data Science In The Power Generation Industry
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Author : Patrick Bangert
language : en
Publisher: Elsevier
Release Date : 2021-01-14
Machine Learning And Data Science In The Power Generation Industry written by Patrick Bangert and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-14 with Technology & Engineering categories.
Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls
Ecmlg 2022 18th European Conference On Management Leadership And Governance
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Author : Florinda Matos
language : en
Publisher: Academic Conferences and publishing limited
Release Date : 2022-11-10
Ecmlg 2022 18th European Conference On Management Leadership And Governance written by Florinda Matos and has been published by Academic Conferences and publishing limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-10 with Business & Economics categories.
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-05
Hands On Machine Learning With Scikit Learn Keras And Tensorflow written by Aurélien Géron 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 2019-09-05 with Computers categories.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Managing Machine Learning Projects
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Author : Simon Thompson
language : en
Publisher: Simon and Schuster
Release Date : 2023-07-11
Managing Machine Learning Projects written by Simon Thompson 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 2023-07-11 with Computers categories.
For anyone interested in better management of machine learning projects from idea to production. Managing Machine Learning Projects is a comprehensive guide that does not require any technical skills. This edition will help you discover battle-tested data infrastructure techniques and will guide you through bringing a project to a successful conclusion.
Artificial Intelligence And Machine Learning In Business Management
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Author : Sandeep Kumar Panda
language : en
Publisher: CRC Press
Release Date : 2021-11-04
Artificial Intelligence And Machine Learning In Business Management written by Sandeep Kumar Panda and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-04 with Business & Economics categories.
Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.
Machine Learning For Managers
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Author : Paul Geertsema
language : en
Publisher: Taylor & Francis
Release Date : 2023-06-19
Machine Learning For Managers written by Paul Geertsema and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-19 with Business & Economics categories.
Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.
Machine Learning Exam Essentials
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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
Optimizing Machine Learning Pipelines Advanced Techniques With Tensorflow And Kubeflow
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-09
Optimizing Machine Learning Pipelines Advanced Techniques With Tensorflow And Kubeflow written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-09 with Computers categories.
'Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow' is the definitive guide for data scientists, AI practitioners, and technology enthusiasts committed to optimizing their machine learning workflows. This meticulously crafted book offers an in-depth exploration of advanced machine learning operations (MLOps), with a strong focus on the practical deployment, monitoring, and management of machine learning models using TensorFlow and Kubeflow. The journey begins with an overview of machine learning fundamentals and the inner workings of TensorFlow. As readers progress, they delve deeper into data preprocessing, feature engineering, and model building, gradually mastering the complexities of fine-tuning and optimizing models for production readiness. The pivotal aspect of automating machine learning pipelines with Kubeflow is thoroughly examined, empowering readers to deploy TensorFlow models with utmost confidence. Furthermore, the book provides valuable insights into advanced TensorFlow techniques, ethical AI development, and model management with TensorFlow Serving, ensuring comprehensive coverage of key topics. 'Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow' is crafted to elevate its readers into proficient MLOps practitioners, adept at harnessing the power of TensorFlow and Kubeflow to deliver impactful AI solutions. Whether you are embarking on your first machine learning project or seeking to enhance your existing AI capabilities, this book is your essential resource for mastering advanced machine learning operations.