Five Key Features for a Machine Learning Platform

Machine learning (ML) platform designers are facing challenges in managing the ML lifecycle as machine learning becomes increasingly prevalent in companies. Many teams start by giving data scientists Jupyter notebooks backed by GPU instances, but this approach breaks down with growing complexity and number of deployments. As a result, more teams are looking for end-to-end ML platforms. Several cloud providers and startups offer these platforms, including AWS (SageMaker), Azure (Machine Learning Studio), Databricks (MLflow), Google (Cloud AI Platform), and others. Ray is a general purpose distributed computing platform that can be used to easily scale existing Python libraries and applications, making it useful for building ML tools and platforms.