Building on our survey-based research report, “How to avoid your looming machine learning crisis”, HFS observes a few patterns emerging for how enterprises can get started with ML, how they can deliver value over time and develop more robust capabilities, and ultimately, build industrialized, ML-enabled operations. The HFS ML Execution Guide below charts out the multi-dimensional capabilities that enterprises will need to develop towards the institutionalization of machine learning. It is important to note that the steps aren’t necessarily linear comparing across the stages. In some cases, enterprises might be more advanced on one dimension, even though they are just getting started. They are meant as a guide for the typical starting and advancement points for enterprise ML adoption.
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