Predictive Analytics World’s ML Times hosted our CPO’s take on managing data science projects. The piece will be presented at their conference in autumn 2020.

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.

Read more at the ML Times