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® Studio as the ideal platform to build your multicloud ModelOps practice.

Try multicloud ModelOps on IBM Cloud Pak® for Data

ModelOps features

What can you do with ModelOps?

Generate a model pipeline leaderboard

Automatically prepare data, select models, perform feature engineering and optimize hyperparameters to generate a pipeline leaderboard.

Monitor machine learning models

Monitor machine learning models by viewing possible model bias and learning how to mitigate it and explain outcomes.

Examine and debias models

Generate a debiased model endpoint and show explainability. Detect data inconsistency leading to model drift.

Deploy model functions with apps

Preprocess data before passing it to models, perform error handling and include calls to multiple models.

Build and deploy models on multiple clouds

Deploy and push models virtually anywhere. Build your own AI-ready cloud using x86, IBM Cloud Pak® for Data System and IBM Power® system.

Build, run and manage models on a unified interface

Prepare data, build models and measure outcomes. Continuously improve models with a feedback loop.

What’s new in multicloud ModelOps?

Webinar: Synchronize DevOps and AI

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

451 Research: AI and ModelOps with automation

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

Developer learning path: Machine learning

Build, run and manage models on a unified data and AI platform. Continuously improve models and use them for your apps.

Product images

KPI comparison

screen shot showing visualization of model comparison, including KPIs, maintenance costs and production

KPI comparison

Compare models against key performance indicators.


screen shot showing how a prediction was determined and the most important factors influencing prediction


See explanations behind AI outcomes.

Pipeline leaderboard

screen shot showing failure prediction for a set of models and a pipeline leaderboard

Pipeline leaderboard

Automatically prepare data, engineer features, optimize parameters and generate a model leaderboard.

Model drift

screen shot showing the model drift magnitude for a German credit risk model

Model drift

Detect and correct model drift in production.

Multicloud versus traditional ModelOps

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