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Microsoft SynapseML Launched: A Rebranded Version of Open-source ML Library MMLSpark

Microsoft Synapse ML is now live. Microsoft has renamed and rebranded its open-source library for scalable machine learning pipelines MMLSpark to SynapseML to attract the open-source DevOps community. The new platform would empower developers to extract more out of scalable ML pipelines and unify each of these into a simplified ecosystem that works well with a variety of AI ML programming languages such as Python, R, Scala, and Java, and others.

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Why SynapseML

Machine learning engineers are on the lookout for tailor-made production-ready ML models. A few years ago, thinking of readymade ML models would have been a crazy proposition. But today, open-source ML libraries are flooding the industry with these solutions, suitably developed with “glue” codes for different ecosystems.

With SynapseML, developers can build scalable and intelligent systems for solving challenges in domains such as:

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• Anomaly detection
• Computer vision
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• Form and face recognition
• Gradient boosting
• Microservice orchestration

• Model interpretability
• Reinforcement learning and personalization
• Search and retrieval
• Speech processing
• Text analytics
• Translation

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Microsoft has released a graphical layout to explain how SynapseML aligns with modern ML development pipelines.

A graphic illustrating that SynapseML unifies a variety of different ML frameworks (including LightGBM, Azure Cognitive Services, Deep Learning, reinforcement learning), scales (including single node, cluster, and serverless + elastic), paradigms (including batch, streaming, and serving), cloud data stores, and languages.

The ML frameworks would further refine industry solutions in Azure Synapse Analytics, making it easy for users to leverage custom-build ML models for a variety of applications, including for e-commerce and retail, healthcare, customer support, telecom, and so on. Microsoft SynapseML would integrate with existing ML systems offered by Microsoft such as Azure Cognitive Services, Light GBM, Vowpal Wabbit, MLFLow, AzureML and other Spark workflows.

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