ML Ops Center Makes Machine Learning Work in Production
INTRODUCING ML OPS CENTER
ParallelM’s ML Ops Center 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
ML Ops Center 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

ML Ops Center is powered by IONS – Intelligence Overlay Networks – 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 contact us to learn more!

HOW IT WORKS
System Components
ML Ops Center is the primary workspace for Operations & Data Science collaborating to ensure ML success in production. It includes intuitive dashboard views, ML Health indicators, KPIs, and advanced visualization and diagnostic tools.
ML Ops Server Orchestrates ML Applications and pipelines via the Agents. It executes policies, manages configuration, and sends data to the ML Ops Center. ML Ops Server enables automation of all the key tasks related to deployment and management of ML.
ML Ops Agents triggers analytics engines and manages local ML pipelines. It provides visibility into the activity of the pipeline and sends alerts, events, and statistics to ML Ops Server. Compatible with popular analytic engines including Spark, TensorFlow, and Flink
Flexible Deployment Options ML Ops Center 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). ParallelM works with you to define the best deployment configuration for your specific needs.
INTEGRATIONS
ML Ops Center leverages your existing tools. Maximize the benefit from investments you have already made in your machine learning infrastructure