Sustainable Rapid Prototyping: Making Machine Learning Projects Reusable
November 18, 2019
In the exploration phase of agile projects, different solution paths are spiked to get an idea on which would be the most suitable one. However, by timeboxing the amount of effort put into every path, you might easily fall into the “greedy”-trap of choosing the easiest approach. More suitable technologies/models that would be too time consuming to fit into the timebox are never considered.
This talk is about reusing results/models of different approaches across other projects and teams to avoid this trap and to speed up the exploration phase. This can be achieved by offering former spike implementations behind an API as a service. If those services are dockerized, tagged, documented, and thus preserved, future exploration phases can shortcut by leveraging the results of already existing models.
Exemplarily, a machine learning classification problem is showcased that uses images on a website rather than text due to circumvent multi-language-problems. It covers the bootstrap-phase of gathering and pre-processing image data, applying transfer learning to the collected data, and offering the resulting neural network via an endpoint.