9798900234786 - mastering the machine: ml for the real world: turning models into impact in the field de dr. rahul sharma (5 resultados)

- Tapa dura
Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 51,54
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.

- Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 70,84
Envío por EUR 62,02Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
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, i…ncomplete, 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.

- Tapa dura
- Impresión bajo demanda
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 51,27
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

- Tapa dura
- Impresión bajo demanda
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 47,76
Envío por EUR 4,82Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
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.

- Tapa dura
- Impresión bajo demanda
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 52,50
Envío por EUR 42,87Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
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 me…ssy, 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.