IBM Data Science for 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 as the ideal platform to build your multicloud ModelOps practice.
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ModelOps benefits
Accelerate end-to-end AI model development. Speed time to value by empowering and reskilling your teams.
Take advantage of AI with a platform approach. Leverage strategic enablers such as automation, prediction and optimization.
Take only minutes to select the top-performing models for cloud-native apps. Track usage statistics and govern model use.
Unify data, talent and tools. Predict and optimize outcomes with visual data science and a natural language interface.
What can you do with ModelOps?
Automatically prepare data, select models, perform feature engineering and optimize hyperparameters to generate a pipeline leaderboard.
Monitor machine learning models by viewing possible model bias and learning how to mitigate it and explain outcomes.
Generate a debiased model endpoint and show explainability. Detect data inconsistency leading to model drift.
Preprocess data before passing it to models, perform error handling and include calls to multiple 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.
Prepare data, build models and measure outcomes. Continuously improve models with a feedback loop.
What’s new in multicloud ModelOps?
Learn why 63% of enterprises adopted DevOps and 33% of them involve data science teams for AI-powered apps.
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. Continuously improve models and use them for your apps.
Product images
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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