Managing AI Project Development via an Agile Framework

AI managers (and leaders managing data science teams) need to think through many questions relating to how to best execute their AI project efforts. For example, how should the team brainstorm ideas, how should the team prioritize those potential ideas, and more generally, how to help ensure the team delivers actionable insights. While these management challenges are very different than technical machine learning challenges that most teams focus on trying to solve, the management challenges are equally important to address to ensure a successful AI project. In other words, the focus of this talk is not on which specific algorithm a team should use, but rather, how to ensure that the AI / data science effort is progressing effectively and efficiently. This talk will discuss how an organization can (and should) establish an agile project framework. The talk will first compare and contrast the challenges of managing AI (and data science projects) with software development projects, as well as examine why a software development process framework might not be appropriate for AI / data science projects. The talk then will explore different frameworks that managers can use to help ensure a successful AI / data science project. By the end of the talk, data scientists and AI managers will understand possible next steps to improve their team’s data science process.
Who is this presentation for?
All (data scientists, managers, ...)
Prerequisite knowledge:
None - equally useful for technical and non technical people
What you'll learn?
By the end of the talk, data scientists and data science / AI managers will understand possible next steps to improve their team’s process.