Machine learning has emerged as a must-have tool for any serious data team: augmenting processes, generating smarter and more accurate predictions, and generally improving our ability to make use of data.

However, discussing applications of ML in theory is much different than actually applying ML models at scale in production. In this article, we walk through common challenges and corresponding solutions to making ML a force multiplier for your data organization.

From generating your weekend bike route on Google Maps to helping you discover your next binge-worthy show on Netflix, machine learning (ML) has evolved well beyond a theoretical buzzword into a powerful technology that most of us use every day.

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