Titlebook: Agile Machine Learning; Effective Machine Le Eric Carter,Matthew Hurst Book 2019 Eric Carter, Matthew Hurst 2019 Agile Software Development

報(bào)復(fù)

Critical Thinking for an Engaged UniversityWhat follows are the principles of the Agile Manifesto (agilemanifesto.org/principles) with our suggested modifications for Agile machine learning teams underlined. We also repeat the recap for each chapter.

腐敗

PUT

Blemish

Tuning and Adjusting,We’ve talked about several mechanisms in this book for looking back and reflecting on past progress with an aim to becoming more effective as individuals, managers, and teams. To recap those mechanisms, they include ., ., ., ., and

Lament

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Early Delivery,ns, and biases?– the data. To successfully execute a project that delivers value through inference over data sets, a novel set of skills, processes, and best practices need to be adopted. In this chapter, we look at the initial stages of a project and how we can make meaningful progress through thes

合唱隊(duì)

Changing Requirements,rns of data will inevitably be outsmarted by future attacks causing the system to become less effective. Ideas about what to do to solve a data problem change as the data is explored and analyzed. Customers can be fickle and change their mind about what they want and what features are most important

感染

Gullible

Aligning with the Business,opean markets attracting 19.6 million unique visitors per month in Europe by 2008. Microsoft purchased Ciao in 2008 to bolster its Bing search engine in Europe. Eric had the opportunity to work with the Ciao team as its new engineering manager after the Microsoft acquisition. What Eric observed was

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Motivated Individuals,iocre results. Several individuals that worked with that conflation engine day in and day out felt there was a better way to do conflation. The existing conflation system had become a bug farm and required a lot of work to keep it working. The new proposed approach seemed promising, but the challeng