Machine Learning Operationalization: A Different Approach to Production ML

Solve Your Production ML Challenges with MLOps

Ready for MLOps? Try MCenter

ParallelM named as one of Gartner's 2018 Cool Vendors in Data Science and Machine Learning

Data Scientists

Get Your ML Experiments to Production While Using Your Existing Data Science Tools and Languages
  • Stop worrying about ML deployment
  • Works how you work – your code, programming language & engines
  • Makes it easy for you to move models into production
  • Provides real insights so you can tune & optimize on real data
  • Uses SDLC practices like source repositories (keep your code in Git)

Operations & Engineering

Manage and Scale Your Production ML Using Established Ops Practices
  • Use DevOps practices you already know
  • Launch new ML apps in your environment
  • Easily help data scientists – without having to be an expert
  • Feel safe deploying ML in production
  • Simplify ML with containerization

Business

Get ROI from ML Initiatives while Managing Risk and Ensuring Compliance
  • Leap ahead of your competition by deploying more ML
  • Finally see results from your ML & AI initiatives
  • Get visibility into ML performance in production
  • Rest easy with advanced governance features
  • Create new ways to leverage ML in your business

MLOps: Manage the Full Lifecycle of ML in Production

Machine learning operationalization (MLOps) provides the fastest and safest path to AI value by automating the deployment, orchestration, and management of machine learning in production

MCenter, our 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, MCenter enables data scientists, IT operations, and business analysts to seamlessly work together on ML initiatives without unnecessary collaboration and communication.

Benefits

Deploy & Manage

Automate pipeline validation with real production data before going live (champion/challenger)

Run multiple pipelines across any infrastructure with a single click

Manage model parameters, configuration & dependencies

Automate Orchestration

Coordinate interaction between inference & training pipelines

Define flexible policies for model updates & pipeline dependencies

Maintain state & behavior awareness across related pipelines

Monitor & Diagnose

Identify inaccurate predictions with ML Health indicators

Resolve issues quickly with model snapshots & rollback

Enhance ML performance with rich visualizations & analytics on ML behavior

Collaborate

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

Data scientists codify expertise into familiar operations workflows

IT & ops personnel drive feedback from live operations back to data scientists & business analysts

Upcoming Events

September 27. 2018
October 2-4, 2018
October 31 - November 3, 2018

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

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