Machine learning is typically viewed as simply training a model on data. However, the “last mile” of deploying models to production systems is often overlooked and yet is one of the most critical aspects of real-world machine learning systems. Despite this, currently there are few widely accepted, open and standard solutions available that cover deployment of end-to-end ML pipelines.
In this talk, we explore the current state of ML deployment using open-source, standardized formats. The talk will cover the various available options, including PMML, PFA and ONNX, and how these fit in with the most popular and widely used ML libraries (including scikit-learn, Spark ML, TensorFlow, Keras and PyTorch).