Titlebook: Machine Learning Governance for Managers; Francesca Lazzeri,Alexei Robsky Book 2024 The Editor(s) (if applicable) and The Author(s), under

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Measuring What Is Relevant,onitoring, such as objectives and key result (OKR), on data science and machine learning efforts. In this chapter, we will go deeper into performance measurements and metrics and emphasize understanding second-level metrics.

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Searching for the Right Algorithms,chine learning (ML) techniques to optimize business metrics, pursue new opportunities, and grow revenues (Lazzeri 2019). Not only has data become more available, but there has also been an explosion of machine learning and artificial intelligence applications that enable companies to build sophistic

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Operationalizing Your Machine Learning Solution,s that can predict and schedule equipment’s maintenance, to intelligent research and development (R&D) systems able to estimate the cost of new drug development, and use human resource (HR) AI-powered tools to enhance the hiring process and employee retention strategies. However, to be able to lever

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,Unifying Organizations’ Machine Learning Vision,r data processing and modeling infrastructure. As more and more data is generated every day by the various telemetry systems, the ability to scale the operations of data science and machine learning becomes increasingly important. However, scaling data processing infrastructure is not as simple as a

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