Machine learning research has much to offer pervasive issues in global and public health. The aim of the workshop is to develop an ecosystem and to provide tangible examples of abstractions which enable Machine Learning (ML) researchers to integrate with global and public health experts through a model driven approach. It also emphasises and scaffolds the science as well as the process to integrate results into an accelerated decision-making workflow. The workshop activities are rooted in knowledge representation and the formal encoding of data and models from multiple problem domains into standardised benchmarks to support the investigation of a diversity of domain questions. We solicit standardised ML solutions to support these domain questions, and also to generate contributions that advance the knowledge representation space in ML. This workshop is a call to action by reflecting on the potential for impact of ML in healthcare and life sciences.