The growing trend to digitalize operations across materials science-driven industries is placing enterprise data science initiatives - especially when it comes to machine learning - in the spotlight. Many organizations are only just beginning to realize the benefits that machine learning offers, yet many more are still struggling to get their projects off the ground. Much of this centers on the scalability of their approach: a shortage of qualified data scientists, the technical complexity of the projects or disconnected IT environments can all be to blame.
In this white paper, ARC explores various best practices for scaling data science and machine learning initiatives across the enterprise and provides different use cases for R&D and manufacturing.
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