Implementing and consuming Machine Learning (ML) techniques at scale are difficult tasks for ML Developers and Domain Experts. MLbase is a platform addressing the issues of both groups. In this talk, we describe the various components of our system, including a low-level distributed machine learning library in Spark, an API for machine learning algorithms and feature extractors, and recent work on higher-level functionality to autotune basic ML pipelines.