Today, we're excited to announce the release of SynapseML (previously MMLSpark), an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. Building production-ready distributed ML pipelines can be difficult, even for the most seasoned developer. Composing tools from different ecosystems often requires considerable "glue" code, and many frameworks aren't designed with thousand-machine elastic clusters in mind. Writing fault-tolerant distributed programs is complex and a process that's prone to errors. For example, consider the distributed evaluation of a deep network.

Microsoft has announced the release of SynapseML, an open-source library that simplifies and speeds up the creation of machine learning (ML) pipelines. SynapseML can be used for building scalable and intelligent systems to solve various types of challenges, including anomaly detection, computer vision, deep learning, form and face recognition, Gradient boosting, microservice orchestration, model interpretability, reinforcement learning, and personalization, search and retrieval, speech processing, text analytics, and translation. SynapseML is a powerful platform for building production-ready distributed machine learning pipelines. It bridges the gap between several existing ML frameworks and Microsoft algorithms in order to create one scalable API that works across Python, R Language-based platforms like Scala or Java. In order to build a machine learning pipeline, you need more than just coding skills.