While there are an increasing number of off-the-shelf machine learning (ML) solutions that promise to adapt to your specific requirements, organizations that are serious about investing in ML for the long term are building their own workflows tailored exactly to their data and the outcomes they expect. To make full use of this investment, ML models must be kept up to date and working from the freshest available data.

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Machine learning (ML) has quickly become integral to many businesses. Its rapid adoption across almost every industry is because it is a force multiplier: new data can be learned from and understood as it arrives, while historical data can be revisited with new tools and practices.