Machine learning (ML) and statistical techniques are key to transforming big data into actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML algorithms is often overwhelming—many users do not understand the trade-offs and challenges of parameterizing and choosing between different learning techniques. Fur- thermore, existing scalable systems that support machine learning are typically not accessible to ML researchers with- out a strong background in distributed systems and low-level primitives. In this work, we present our vision for MLbase, a novel system harnessing the power of machine learning for both end-users and ML researchers. MLbase provides (1) a simple declarative way to specify ML tasks, (2) a novel opti- mizer to select and dynamically adapt the choice of learning algorithm, (3) a set of high-level operators to enable ML re- searchers to scalably implement a wide range of ML methods without deep systems knowledge, and (4) a new run-time optimized for the data-access patterns of these high-level operators.