Machine Learning Operationalization:
A Different Approach to Production ML
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March 6-8, 2018
March 19-21, 2018
May 9-10, 2018
ParallelM provides the fastest and safest path to AI value by automating the deployment, orchestration, and management of Machine Learning in Production
ParallelM’s MLOps software platform drives a repeatable, scalable Machine Learning Management Lifecycle to minimize the risk and complexity of AI.
As the central repository of all your Machine Learning activity in production, ParallelM enables collaboration across data scientists, IT operations, and business analysts.
Benefits
Deploy & Manage

Validate pipelines with real production data before going live

Run multiple pipelines across any infrastructure with a single click

Manage model parameters and configuration

Automate Orchestration

Coordinate interaction between inference and training pipeline

Define flexible policies for model updates and pipeline dependencies

Maintain state and behavior awareness across related pipelines

Monitor & Diagnose

Identify inaccurate predictions with ML Health indicators

Resolve issues quickly with model snapshots and rollback

Enhance ML performance with rich visualizations and analytics on ML behavior

Collaborate

Diverse teams can communicate with shared and role-based dashboard views

Data scientists codify expertise into familiar operations workflows

MLOps drive feedback from live operations back to data scientists and business analysts