Jumpstart Machine Learning with Pre-Trained Models
As a community many of us are building models (statistical and machine learning) that address various scenarios. At conferences, like UseR!, but also across many academic conferences, researchers publish papers that introduce new algorithms with implementations available on GitHub, implemented in R and Python and other frameworks. The community also makes available pre-trained models, especially deep learning models, to demonstrate or highlight the capabilities of the algorithm. To foster a healthy collaboration and for the reproducibility of key results, it is important that fellow data scientists can read about a new algorithm or approach and to be able to try it out very quickly to see whether it meets their needs. While pre-trained machine learning models are available, they are often difficult to set up and evaluate. We are exploring a framework to make this process simpler by making it easy for any data scientist to investigate and evaluate pre-trained models. We will share our learnings and our proposal to enable data scientists to quickly discover pre-trained models that will support them to be able to get from zero to hero in short order.