MCenter Makes Machine Learning Work in Production

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ParallelM’s MCenter helps Data Scientists deploy, manage and govern ML models in production. Just import your existing model from your favorite notebook and then create data connections or a REST endpoint for model serving with the drag-and-drop pipeline builder. Advanced monitoring automatically creates alerts when models are not operating as expected due to changing data. With built-in model governance, every action is controlled and tracked including model versioning and who can promote models into production to ensure compliance with regulations.
Automate Deployment
ML Health
MCenter dashboard displays ML Health, alerts, and key performance indicators
Deep diagnostic templates with data & model snapshots ensure rapid problem resolution & ML continuity

MCenter makes it easy to deploy machine learning models with pre-built components and a pipeline builder to create combinations of pipelines called MLApps.

Production Pipeline Builder – MCenter comes with a library of components and a drag-and-drop pipeline builder so you can build production pipelines in minutes not hours.

Advanced ML Health Monitoring – MCenter automatically alerts you when production data deviates from training data or when your model’s results start to drift apart from a canary model you trust so you can focus on building new models and only update models when needed.

Built-in A/B Testing – Prove that new models are better than incumbents with built-in A/B testing with easy to read results.

Complete Model Governance – MCenter includes control and tracking for all actions taken in the system so you can control who can put models into production and see what model provided a given prediction.

Building Advanced MLApps – With MCenter you can combine multiple pipelines together into an ML application to serve your business use case. Common pipeline combinations include automated batch retraining with REST inference, ensemble models, and sequential pipelines where multiple pipelines feed each other to create an output.

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MCenter Agents – MCenter agents trigger analytics engines and manage local ML pipelines. They provide visibility into the activity of the pipeline and sends alerts, events, and statistics to the MCenter server. They are compatible with popular analytic engines including Spark, TensorFlow, and Flink

MCenter Server – The MCenter server orchestrates ML Applications and pipelines via the MCenter agents. It executes policies, manages configuration, and sends data to the MCenter console. The MCenter server enables automation of all the critical tasks related to the deployment and management of ML.

Flexible Deployment Options – MCenter can be deployed in the cloud, on-premise, or in hybrid scenarios. It also works across distributed computing architectures that include inter-operating, various analytic engines (Spark, TensorFlow, Kubernetes, PyTorch, and more). ParallelM works with you to define the best deployment configuration for your specific needs.

MCenter leverages your existing data science tools, languages, and infrastructure to maximize the benefit from investments you have already made.