Agile Machine Learning with DataRobot: Automate each step of the machine learning life cycle, from understanding problems to delivering value

Chadha

Juwe

£41.99

4.8 (11 Ratings)

Paperback Dec 2021 344 pages 1st Edition

Chadha

Juwe

£41.99

4.8 (11 Ratings)

Paperback Dec 2021 344 pages 1st Edition

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Key benefits

Get well-versed with DataRobot features using real-world examples
Use this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycle
Make use of advanced DataRobot capabilities to programmatically build and deploy a large number of ML models

Description

DataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization. You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities. By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors.

Who is this book for?

This book is for data scientists, data analysts, and data enthusiasts looking for a practical guide to building and deploying robust machine learning models using DataRobot. Experienced data scientists will also find this book helpful for rapidly exploring, building, and deploying a broader range of models. The book assumes a basic understanding of machine learning.

What you will learn

Understand and solve business problems using DataRobot
Use DataRobot to prepare your data and perform various data analysis tasks to start building models
Develop robust ML models and assess their results correctly before deployment
Explore various DataRobot functions and outputs to help you understand the models and select the one that best solves the business problem
Analyze a model s predictions and turn them into actionable insights for business users
Understand how DataRobot helps in governing, deploying, and maintaining ML models

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Amazon verified reviews

M & M Jan 13, 2022

Nice flow from introduction to what’s new part!

Amazon Verified review

Amazon Customer Mar 23, 2022

If accelerating development & deployment cycles for machine learning models is a priority, then this book is an extremely valuable resource. The book brings together key concepts in data products, data engineering, machine learning and MLOps, and uses real-life use cases to demonstrate how platforms like DataRobot can drive automation, best practices and value-creation. It’s great to see the focus on scalability and MLOps-related practices, as too many books miss the mark on these fronts.Overall, an extremely effective text for those building ML-powered data products for internal or external monetization.

Robert Welborn Mar 29, 2022

I received a complimentary copy of this book to do the review, but it's a book I would readily purchase. This book is a great text for the technical leader/architect and the ML engineer. It is not an intro to DataRobot or even machine learning. It's a good mix of the strategy of execution and deployment.DataRobot is a great tool, but the wide-open nature leads of the tool frequently leads to teams taking an inefficient processing approach, which can lead to much slower iteration. The agile approach in the book will save you time lead to more impactful iterations.

Leo Sep 19, 2022

Before talking about the book itself, a word about how cool and exciting the subject of this book really is. Automated machine learning is a huge step forward and right now DataRobot is at the forefront of this change. Even for projects which cannot be fully automated, partial automation in crucial areas can provide huge benefits, such as higher productivity, more depth and rigor, and better structure.About the book. The organization of the book is clear and logical. The first section starts by motivating the use of automated ML and DataRobot and laying out the foundations. The second section takes the reader through the typical lifecycle of an ML project; such a lifecycle is in fact very much built into the tool itself. The third section presents advanced topics with a set of specific examples.One of the major strengths of the book is that it puts the material in a larger context, that of providing actionable solutions for stakeholders. It thoroughly motivates each phase of the ML lifecycle and shows how the interaction between the automation and other methodologies can occur, for instance causal modeling and model governance.Another strength is that the authors have a fair and balanced approach: DataRobot is never presented as a panacea, but instead the benefits and limitations of the tool are carefully and objectively described.To sum up, this is a great book for people learning DataRobot but also for practitioners who do not have a good idea of what automated ML can provide. Additionally there is a lot of material that will challenge the ideas and incite reflection irrespective of the level of experience.Really worth the read.Disclaimer: I have worked in the past with the authors.

Espanol 123 May 04, 2022

Today, Machine Learning is thrown around as a ubiquitous buzzword, often without context or explanation, so there is a real need for demystifying this term, as well as what Machine Learning really is and how business leaders can exploit its value. Agile Machine Learning with DataRobot is a book whose timing fills this need perfectly. It explains the robust, business-changing results that Machine Learning can deliver, while writing about it in a way that is technical enough for a practitioner to take their skills to the next level, and digestible enough for those who are starting out as new data scientists.The authors expect the reader to have a basic familiarity of Machine Learning concepts, as they take the reader step-by-step through the analytical process and introduce techniques with the DataRobot software. In doing so, they open our understanding for how we consume and process the information that forms our Machine Learning worldview, from concept to deployment.The authors, Bipin Chadha and Sylvester Juwe, are data science leaders and executives, who are known for their strong grasp of models and their implications for business. Dr. Chadha is a hands-on data leader, who builds models and teams, and also endeavors to build data-driven cultures. Dr. Juwe is a highly accomplished executive with deep technical expertise in implementing advanced analytical solutions. Together, the two authors combine the technical with the executive mindset, demystifying the world of Machine Learning through the practical application of the analysis process with DataRobot. The insights provided by this book, from building a strong foundational understanding of the business problem to building models that approach human cognition, make it a must-read for students, practitioners, and executives, as well as risk offices and regulators who are designing policies for how to manage models where automation is used in the Machine Learning process.Machine Learning models can improve our ability to explain events around us, but no single model can explain all phenomena, which is why DataRobot can be a powerful tool for generating many models for our understanding. It is appropriate that the book is organized along three sections: “Foundations,” “ML Life Cycle,” and “Advanced Topics.” Each chapter of “Foundations” builds from basic concepts, starting with the business problem, DataRobot fundamentals, data preparation, and Machine Learning basics. “Full ML Life Cycle with DataRobot: Concept to Value” is exactly what is says, taking the reader from data sourcing and exploratory data analysis to model building, understanding, scoring, and deployment. The “Advanced Topics” section elaborates even further with forecasting, time series, and recommender models, to working with geospatial, text, and image data. The book ends with a discussion on governance and MLOps, terms that will certainly come to light as Machine Learning becomes more widespread. With each chapter, the authors provide examples that are made comprehensible through concepts, followed by real-world DataRobot examples. Compared to other books on Machine Learning models, Agile Machine Learning with DataRobot is approachable for experienced and new data scientists alike.We live in a time that is flooded in information, and this can sometimes make the relationships between cause and effect harder to comprehend, or risk oversimplification of the problem. Agile Machine Learning with DataRobot patiently evolves from our understanding of the business problem, into a deeper roadmap for understanding the Machine Learning lifecycle, the analytical process with DataRobot, and the explainability of these models. Using the DataRobot software, Chadha and Juwe show how we can identify the optimal factors and create automated Machine Learning models that will lead to better results for businesses. I recommend this book to anyone wishing to improve their understanding of Machine Learning via the DataRobot software, whether they are new data scientists who can become more productive in a shorter time frame, or experienced practitioners, whose jobs can be made easier through Machine Learning automation and MLOps.

Agile Machine Learning with DataRobot: Automate each step of the machine learning life cycle, from understanding problems to delivering value

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