AI and ML Watson Studio IBM Data Science for ModelOps
Synchronize DevOps and ModelOps. Build and scale AI models with your cloud-native apps across virtually any cloud.
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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 as the ideal platform to build your multicloud ModelOps practice.

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ModelOps benefits Automate AI lifecycle management

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

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Speed time to AI outcomes

Take advantage of AI with a platform approach. Leverage strategic enablers such as automation, prediction and optimization.

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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.

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Simplify onboarding

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

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What can you do with ModelOps? Learn more about 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.

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451 Research: AI and ModelOps with automation

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

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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.

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KPI comparison            Compare models against key performance indicators.

Explanations See explanations behind AI outcomes.

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

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

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