I am really excited about the first book on Enterprise AI products called DataRobot. The book is useful for people entering in Data Science industry with a DataRobot account sponsored by their employer.I think this book will be most helpful for people that have a DataRobot account which usually means their company is already a DataRobot customer, but for those people, this will be a big help to get them up and running and get the most out of their investment.The book is relevant to a lot of data scientists in providing best practices in solving different business problems. Topics like- Framing the business problems data science problem- Handling data prior to training- Selecting model performance metrics for deploymentare all relevant regardless of the platform of choice.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.

I really enjoyed the introduction to get a level-set on definitions and technology. There is so much information in this space that it is good to establish a baseline.I think this book will be most helpful for people that have a DataRobot account which usually means their company is already a DataRobot customer, but for those people, this will be a big help to get them up and running and get the most out of their investment.I did receive a complimentary copy so that I could do a review, but this is my review and I would not hesitate to spend the money to buy this book if I was working with DataRobot.My biggest question is how long will this book be useful before there are too many changes in the product.

Nice flow from introduction to what’s new part!

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.

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.