MCenter Makes Machine Learning Work in Production
MCenter: AN ENTERPRISE-GRADE MLOps PLATFORM
ParallelM’s MCenter moves ML pipelines into production, automates orchestration, and guarantees machine learning performance 24/7. It is the single solution where data scientists, IT operations, and business analysts come together to automate, scale, and optimize machine learning across the enterprise.
Automate Deployment
MONITOR AND MANAGE
ML Health
MCenter dashboard displays ML Health, alerts, and key performance indicators
ORCHESTRATE
Diagnose
Collaboration
Deep diagnostic templates with data & model snapshots ensure rapid problem resolution & ML continuity
TECHNOLOGY

MCenter is powered by MLApps – a new paradigm for machine learning management

ParallelM has developed patent pending innovations in production model management and collaboration, automated analytics of live ML prediction quality, ML orchestration, and heterogenous engine/language support.

More technology information will be released soon. Stay tuned or schedule a demo to learn more!

HOW MCenter WORKS
System Components
The MCenter workspace enables collaboration between operations & data science teams to ensure ML success in production. It includes intuitive dashboard views, ML Health indicators, KPIs, and advanced visualization and diagnostic tools.
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 key tasks related to deployment and management of ML.
The 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
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, diverse analytic engines (Spark, TensorFlow, Flink, PyTorch). ParallelM works with you to define the best deployment configuration for your specific needs.
INTEGRATIONS
MCenter leverages your existing tools. Maximize the benefit from investments you have already made in your machine learning infrastructure
ParallelM MCenter Integrations

Try MCenter and See How Much Easier ML In Production Can Be

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