ISBN: 9781009124201 - shipping machine learning systems: a practical guide to building, deploying, and scaling in production (22 resultados)

paperback. Condición: Fine.

Condición: New.

paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New. This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team.…

Condición: New.

Condición: New. 2026. paperback. . . . . .

Paperback. Condición: Brand New. 1st edition. 446 pages. 6.00x0.90x9.00 inches. In Stock.

Paperback / softback. Condición: New. New copy - Usually dispatched within 3 working days.

Condición: As New. Unread book in perfect condition.

Condición: New. 2026. paperback. . . . . . Books ship from the US and Ireland.

Condición: New.

Paperback. Condición: new. Paperback. This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team. This book by industry leaders is ideal for professionals and students seeking a clear, practical understanding of machine learning in real-world settings. Through insightful real-world examples, business case studies, and straightforward practical guidance, readers gain essential skills to implement machine learning effectively in industry. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team.…

Paperback. Condición: New. This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team.…

Paperback. Condición: new. Paperback. This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team. This book by industry leaders is ideal for professionals and students seeking a clear, practical understanding of machine learning in real-world settings. Through insightful real-world examples, business case studies, and straightforward practical guidance, readers gain essential skills to implement machine learning effectively in industry. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Paperback. Condición: new. Paperback. This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team. This book by industry leaders is ideal for professionals and students seeking a clear, practical understanding of machine learning in real-world settings. Through insightful real-world examples, business case studies, and straightforward practical guidance, readers gain essential skills to implement machine learning effectively in industry. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condición: Neu. Shipping Machine Learning Systems | Mohamed El-Geish (u. a.) | Taschenbuch | Englisch | 2026 | Cambridge University Press | EAN 9781009124201 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…