Overview
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 benefits
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
KPI comparison
Compare models against key performance indicators.
Explanations
Explanations
See explanations behind AI outcomes.
Pipeline leaderboard
Pipeline leaderboard
Automatically prepare data, engineer features, optimize parameters and generate a model leaderboard.
Model drift
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 |
Get started
Explore IBM Watson Studio for IBM Cloud Pak for Data.