Название: Reliable Machine Learning: Applying SRE Principles to ML in Production (Early Release)
Автор: Cathy Chen, Kranti Parisa, Niall Richard Murphy
Издательство: O’Reilly Media, Inc.
Год: 2021-10-12
Страниц: 93
Язык: английский
Формат: epub
Размер: 10.2 MB

Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run 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.

Most of this book is about managing machine learning systems and production level ML pipelines. This involves work that is quite different from the work often performed by many data scientists and machine learning researchers, who ideally spend their days trying to develop new predictive models and methods that can squeeze out another percentage point of accuracy. Instead, in this book, we focus on ensuring that a system that includes an ML model exhibits consistent, robust, and reliable system level behavior. In some ways, this system level behavior is independent of the actual model type, how good the model is, or other model-related considerations. Still, in certain key situations, it is not independent of these considerations. Our goal in this chapter is to give you enough background to understand which situation you are in when the alarms start to go off or the pagers start to fire for your production system

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 guests show you how to run an efficient 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 on
Conceptual frameworks for understanding how ML "loops" work
Effective "productionization," and how it can be made easily monitorable, deployable, and operable
Why ML systems make production troubleshooting more difficult, and how to get around them
How ML, product, and production teams can communicate effectively