Introduction With the huge growth in machine learning over the past few years, there is a lot of discussion, but few frameworks, on effective AI Project Management. Industry-standard frameworks for data analysis projects, like CRISP-DM, exist but none are effective for managing the development of AI products from deployment to production. The result is that many data science teams are focused on outputting one-off analytical projects, rather than building long-term, maintainable products that directly drive business processes and goals. Luckily, the software engineering world has spent decades grappling with the challenges of building products at scale, and the machine learning
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