Machine Learning Operationalization: A Different Approach to Production ML

Data Scientists

Easily Deploy, Observe & Optimize Models in Production
  • Deploy using your existing platforms like Python, Jupyter, and Git
  • See what works in production with deep real-time monitoring
  • Optimize model performance with advanced ML Health algorithms and A/B Testing
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Operations & Engineering

Scale Machine Learning Applications in Production
  • Use Docker containers with Kubernetes and other existing tools to scale model deployment
  • Deploy anywhere – any cloud, hybrid, on-premise or airgap
  • Works with your existing ops infrastructure like LDAP, Jira and Cron and key ops concepts like role-based access control

Technical Executives

Unlock the Value of AI for Your Business
  • Put AI to work in your organization today without adding headcount
  • Ensure compliance with new regulations with built-in ML governance policies
  • Show that ML models are delivering ROI and improving key business metrics

Manage the Full Lifecycle of ML in Production

MLOps is a new practice including people, process and technology that allows you to quickly and safely deploy and optimize machine learning applications (MLApps) at scale.

ParallelM MCenter, the leading MLOps platform, provides the fastest and safest path to AI value by automating the deployment, ongoing optimization, and governance of machine learning applications in production.

MCenter drives a repeatable, scalable machine learning lifecycle to minimize the risk and complexity of AI so you can deliver results today and scale for tomorrow.

As the central repository of all your machine learning activity in production, MCenter enables data scientists, IT operations, and business stakeholders to unlock the value of MLApps at scale.

The Benefits of MCenter

Easy Model Deployment

Use your existing data science notebook or workbench like Jupyter

Integrations to existing engines like Spark, Flink, TensorFlow or use Docker

Integrates with current SDLC tools like Git, Bitbucket, and Cron

Optimize Model Performance

Advanced ML Health with automatic data drift detection using patent-pending algorithms

Champion-challenger and control/canary pipeline testing built-in

Seamless orchestration of multiple pipelines including training and inference to ensure models stay up to date, automatically

Production Scale

Operates in the control plane with a lightweight footprint and low overhead

Easily scales to manage thousands of model pipelines in production

Supports multiple ML applications that use the same basic pipelines

Model Governance Built In

Rich model performance data with over 150 attributes monitored

Pipeline Snapshots capture critical moments for analysis

Full logging and audit trail for reporting and regulatory compliance

Upcoming Events

December 3-8, 2018
December 5 - 6
December 17-20, 2018

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

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