C3 AI Platform

Model Development

Build production-ready models quickly

Enterprise-Grade Pandas Interface

Start exploring and prototyping with large datasets on day one.The C3 AI Platform makes it easy to transition from experimentation to production with the ability to load and explore datasets from enterprise sources, define machine learning features, and ensure traceability with auto-recorded experimentation steps and metadata.​​

Learn more about data exploration on the C3 AI Platform​

Managed Notebooks

Data science teams can easily collaborate on and manage end-to-end machine learning lifecycles with feature-rich JupyterLab notebooks and C3 AI Python SDK.

Teams can develop runtimes from open-source or private repositories, choose compute requirements, and manage notebook versioning and access controls.​​

Open Source and Plugins

The C3 AI Platform makes it easy to work with the best and most common development tools and libraries to develop the highest-performing models. Data scientists can define runtime environments choosing from 30+ pre-integrated libraries or additional public or private repositories. Users can also integrate pre-trained models and additional services to improve performance.​​

Optimization

Quickly develop optimization models using an out-of-the-box and field-tested foundation. Leverage leading solvers such as Gurobi or pre-built models and application code of C3 AI Applications.​​​

Learn more about C3 AI Applications

Composable ML Pipelines

Increase productivity and develop ML models faster with a library of pre-built components for various use cases, including anomaly detection, natural language processing, and time series forecasting. Easily track performance across experiments and models with built-in interpretability and performance metrics.​​

AutoML

Rapidly build and deploy high-performing AI models with one click — spend less time on low-level details and more time improving business outcomes.

Automatically generate and select features, optimize model hyperparameters, and train models across regression, classification, or clustering problems.​​​

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