Extract from Ben Lorica and Mike Loukides’ article “You created a machine learning application. Now make sure it’s secure”
In a recent post, we described what it would take to build a sustainable machine learning practice. By “sustainable,” we mean projects that aren’t just proofs of concepts or experiments. A sustainable practice means projects that are integral to an organization’s mission: projects by which an organization lives or dies. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it’s important, and what it’s capable of accomplishing. Finally, sustainable machine learning means that as many aspects of product development as possible are automated: not just building models, but cleaning data, building and managing data pipelines, testing, and much more. Machine learning will penetrate our organizations so deeply that it won’t be possible for humans to manage them unassisted.
