Try multicloud ModelOps

What is multicloud ModelOps? Why now?

By 2023, 70% of AI workloads will use application containers or be built using a serverless programming model necessitating a DevOps culture.*

ModelOps is a principled approach to operationalizing a model in apps. ModelOps synchronizes cadences between the application and model pipelines. With multicloud ModelOps you can optimize your data science and AI investments using data, models and resources from edge to core to cloud.

Multicloud ModelOps covers the end-to-end lifecycles for optimizing the use of models and applications across clouds, targeting machine learning models, optimization models and other operational models to integrate with Continuous Integration and Continuous Deployment (CICD). IBM Cloud Pak™ for Data uses IBM Watson® StudioWatson Machine Learning and Watson OpenScale as the ideal platform to build your multicloud ModelOps practice.


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What’s new in multicloud ModelOps

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On-demand webinar: Synchronize DevOps and AI

Learn why 63% of enterprises adopted DevOps and 33% of them involve data science teams for AI-powered apps.

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451 Research: AI and ModelOps with intelligent automation

Gain insights and pragmatic tips from AI pioneers on how to build ModelOps in the multicloud environment.

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Build, run and manage models on a unified data and AI platform

Prepare data, build models and measure outcomes. Continuously improve models and use them for your apps.

See what you can do inside IBM Data Science multicloud ModelOps

Comparison Table

Comparison table
  Multicloud ModelOps Traditional ModelOps
Multicloud support   
Automated AI lifecycle   
Business KPI monitoring   
Explainability & de-biasing   
Drift direction & measurement   
One-click deployment with CICD   
Model management & feedback   
Advanced data refinery   
Data preparation