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® Studio, Watson Machine Learning and Watson OpenScale as the ideal platform to build your multicloud ModelOps practice.
Multicloud ModelOps benefits
Multicloud ModelOps features
What’s new in multicloud ModelOps
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.
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.
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
Compare models against KPIs.
See explanations behind AI outcomes.
Automatically prepare data, engineer features, optimize parameters, and generate a model leaderboard.
Detect and correct model drift in production.
Comparison Table
Explore IBM Watson Studio for IBM Cloud Pak for Data
Footnote
*Source: Gartner. Chirag Dekate, et al. Predicts 2019: Artificial Intelligence Core Technologies, November 2018 (link resides outside IBM)