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Artículo: AMZ-1098106229
Reliable Machine Learning: Applying SRE Principles to ML in Production
Format:
Paperback
Detalles del producto
Disponibilidad
En stock
En stock
Peso con empaque
1.12 kg
1.12 kg
Devolución
Sí
Sí
Condición
Nuevo
Nuevo
Producto de
Amazon
Amazon
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USA
USA
Sobre este producto
- Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. You'll examine: What ML is: how it functions and what it relies onConceptual frameworks for understanding how ML "loops" workHow effective productionization can make your ML systems easily monitorable, deployable, and operableWhy ML systems make production troubleshooting more difficult, and how to compensate accordinglyHow ML, product, and production teams can communicate effectively
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