Enterprise data scientists are frustrated by the Sisyphean struggle to get the technology assets they require to build data models. But that’s hardly the only hurdle: Because these projects slow-cook in siloes, data science teams often duplicate efforts. It’s a maddening combination of requisitioning hell and redundancies.
No stranger to such challenges, defense contractor Lockheed Martin installed a software platform to make the development of machine learning (ML) and artificial intelligence (AI) models more efficient. The platform centralizes assets required to build data models, reducing the costs of the company’s ML and AI projects by $20 million a year, says Matt Seaman, Lockheed Martin’s chief data and analytics officer of enterprise operations.
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Source: News