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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, Watson Machine Learning and Watson OpenScale as the ideal platform to build your multicloud ModelOps practice.

 

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Multicloud ModelOps benefits

Automate AI lifecycle management

Accelerate end-to-end AI model development. Speed time to value by empowering and reskilling your teams.

Explore AutoAI (Link resides outside IBM)

Speed time to AI outcomes

Link model performance to business KPIs. Operationalize AI models across clouds. Choose among clouds for AI learning and inferencing.

Make AI outcome-driven

Monitor fairness and mitigate model drift

Explain how models are derived. Measure fairness and help correct drift from the desired baseline.

Manage AI models (00:58)

Make AI ready for DevOps

Take only minutes to select the top-performing models for cloud-native apps. Track usage statistics and govern model use.

View infographic (PDF, 610 KB)

Optimize cloud and AI model economics

Put multicloud AI to work for business. Use flexible consumption models.

Read ESG report

Simplify on boarding

Unify data, talent and tools. Predict and optimize outcomes with visual data science and a natural-language interface.

View brochure (PDF, 1.8 MB)

Multicloud ModelOps features

Generate a model pipeline leaderboard

Automatically prepare data, select models, perform feature engineering and optimize hyperparameters.

Find top-performing models now

Monitor machine learning models with Watson OpenScale

View possible model bias, learn how to mitigate it and how to explain outcomes.

Take a 30-minute tour

Examine and debias models

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

Get hands-on with Watson OpenScale

Deploy model deployment functions with apps

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

Use REST APIs

Mix and match clouds to build and deploy models

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

Start in a few hours

Build, run and manage models on a unified interface

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

Get on a learning path now

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.

Webinar: Get up to speed on ModelOps (Link resides outside IBM)

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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.

Register to read 451 report

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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.

Get started with machine learning now

See what you can do inside IBM Data Science multicloud ModelOps

Screenshot of preventive maintenance visualization

Compare models against KPIs.
Screenshot showing confidence level data

See explanations behind AI outcomes.
Screenshot showing data from intelligent maintenance function

Automatically prepare data, engineer features, optimize parameters, and generate a model leaderboard.
Screenshot showing drift in German Credit Risk Model

Detect and correct model drift in production.
Screenshot of preventive maintenance visualization

Compare models against KPIs.

Screenshot showing confidence level data

See explanations behind AI outcomes.

Screenshot showing data from intelligent maintenance function

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

Screenshot showing drift in German Credit Risk Model

Detect and correct model drift in production.

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

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Footnote

*Source: Gartner. Chirag Dekate, et al. Predicts 2019: Artificial Intelligence Core Technologies, November 2018 (link resides outside IBM)