Mastering the Machine: ML for the Real World explores the practical challenges and strategies for implementing machine learning systems beyond controlled research environments. While academic ML often focuses on clean datasets and benchmark accuracy, real-world applications must deal with messy, incomplete, and constantly evolving data. The book emphasizes that success in production ML is less about achieving the highest model accuracy and more about building systems that are scalable, reliable, interpretable, and aligned with business goals. Key themes include the importance of data quality and preprocessing, as most real-world effort goes into cleaning, balancing, and engineering features rather than model selection alone. The text highlights data drift, concept drift, and feedback loops, showing how models degrade over time without proper monitoring and retraining. It also covers model evaluation, stressing that accuracy is insufficient for imbalanced datasets and that fairness, interpretability, and business KPIs must guide decision-making. Overall, the work positions machine learning as not just a technical challenge but a socio-technical system requiring collaboration among data scientists, engineers, and domain experts.
"Sinopsis" puede pertenecer a otra edición de este libro.
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. del artículo: L1-9798900234786
Cantidad disponible: Más de 20 disponibles
Librería: California Books, Miami, FL, Estados Unidos de America
Condición: New. Nº de ref. del artículo: I-9798900234786
Cantidad disponible: Más de 20 disponibles
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. del artículo: L1-9798900234786
Cantidad disponible: Más de 20 disponibles
Librería: CitiRetail, Stevenage, Reino Unido
Hardcover. Condición: new. Hardcover. Mastering the Machine: ML for the Real World explores the practical challenges and strategies for implementing machine learning systems beyond controlled research environments. While academic ML often focuses on clean datasets and benchmark accuracy, real-world applications must deal with messy, incomplete, and constantly evolving data. The book emphasizes that success in production ML is less about achieving the highest model accuracy and more about building systems that are scalable, reliable, interpretable, and aligned with business goals. Key themes include the importance of data quality and preprocessing, as most real-world effort goes into cleaning, balancing, and engineering features rather than model selection alone. The text highlights data drift, concept drift, and feedback loops, showing how models degrade over time without proper monitoring and retraining. It also covers model evaluation, stressing that accuracy is insufficient for imbalanced datasets and that fairness, interpretability, and business KPIs must guide decision-making. Overall, the work positions machine learning as not just a technical challenge but a socio-technical system requiring collaboration among data scientists, engineers, and domain experts. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798900234786
Cantidad disponible: 1 disponibles
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Buch. Condición: Neu. Neuware - Mastering the Machine: ML for the Real World explores the practical challenges and strategies for implementing machine learning systems beyond controlled research environments. While academic ML often focuses on clean datasets and benchmark accuracy, real-world applications must deal with messy, incomplete, and constantly evolving data. The book emphasizes that success in production ML is less about achieving the highest model accuracy and more about building systems that are scalable, reliable, interpretable, and aligned with business goals. Key themes include the importance of data quality and preprocessing, as most real-world effort goes into cleaning, balancing, and engineering features rather than model selection alone. The text highlights data drift, concept drift, and feedback loops, showing how models degrade over time without proper monitoring and retraining. It also covers model evaluation, stressing that accuracy is insufficient for imbalanced datasets and that fairness, interpretability, and business KPIs must guide decision-making. Overall, the work positions machine learning as not just a technical challenge but a socio-technical system requiring collaboration among data scientists, engineers, and domain experts. Nº de ref. del artículo: 9798900234786
Cantidad disponible: 2 disponibles