Productivity, the economic measure of output per unit of input
Productivity improvements can come when your data scientist and your team can focus on mainly high-value activities like modeling, insight and the business domain areas and outcomes, rather than all the support processes. Every percentage point of effort that can be shifted in this equation improves productivity dramatically. Freeing data scientists up for more value-add tasks has a direct impact on entire teams and the project overall.
There’s definitely healthy potential to shift the formula and “upskill” the talent pool. For example, moving toward “productizing” tasks would allow more narrowly skilled or less experienced team members (such as developers) to take more involved and leading roles. Productizing allows the platform (versus the individual) to undertake more tasks such as experiment management and auditing — and to learn and evolve in responsibility over time, taking over more and more of these tasks.
Giving AI teams an easy to use end-to-end platform that’s GUI-based is another way to reduce the overall effort and free up data science teams to focus on more high-value tasks. In addition, speeding up the experimentation cycle has big impact — reducing the time to import and process data, create a deep learning model to test in minutes and running tests on models quickly and in parallel.
In AI projects, productivity metrics hinge on freeing up your data science resources to focus exclusively on high-value activities, shifting the 90/10 factor and eliminating the lower-value support functions from their repertoire.