Spark pipelines represent a powerful concept to support productionizing machine learning workflows. Their API allows to combine data processing with machine learning algorithms and opens opportunities for integration with various machine learning libraries. However, to benefit from the power of pipelines, their users need to have a freedom to choose and experiment with any machine learning algorithm or library. Therefore, we developed Sparkling Water that embeds H2O machine learning library of advanced algorithms into the Spark ecosystem and exposes them via pipeline API. Furthermore, the algorithms benefit from H2O MOJOs - Model Object Optimized - a powerful concept shared across entire H2O platform to store and exchange models. The MOJOs are designed for effective model deployment with focus on scoring speed, traceability, exchangeability, and backward compatibility. In this talk we will explain the architecture of Sparkling Water with focus on integration into the Spark pipelines and MOJOs. We'll demonstrate creation of pipelines integrating H2O machine learning models and their deployments using Python. Furthermore, we will show how to utilize pre-trained model MOJOs with Spark pipelines.