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#Topics Bridging the expectation-reality gap in machine learning [ad_1] There is no quick-fix to closing this expectation-reality gap, but the first step is to foster honest dialogue between teams. Then, business leaders can begin to democratize ML across the organization. Democratization means both technical and non-technical teams have access to powerful ML tools and are supported with continuous learning and training. Non-technical teams get user-friendly data visualization tools to improve their business decision-making, while data scientists get access to the robust development platforms and cloud infrastructure they need to efficiently build ML applications. At Capital One, we’ve used these democratization strategies to scale ML across our entire company of more than 50,000 associates. When everyone has a stake in using ML to help the company succeed, the disconnect between business and technical teams fades. So what can companies do to begin democratizing ML? Here are several best practices to bring the power of ML to everyone in the organization. Enable your creators The best engineers today aren’t just technical whizzes, but also creative thinkers and vital partners to product specialists and designers. To foster greater collaboration, companies should prov...

Bridging the expectation-reality gap in machine learning - AIPressRoom

Bridging the expectation-reality gap in machine learning - AIPressRoom

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