Overview

Deploy, monitor, and scale AI with confidence

Foundation takes your models from experimentation to production with enterprise-grade reliability and governance.

Deploy Production ML Models: Move trained models from Jupyter notebooks to scalable production endpoints

Monitor Model Performance: Track accuracy degradation, data drift, and business KPI impact in real-time

Automate Data Engineering: Use AI agents to clean data, detect anomalies, and optimize pipelines

Manage Model Lifecycle: Version control, rollback capabilities, and automated retraining workflows

Scale AI Operations: Deploy models across multiple environments with consistent configuration

Ensure Model Governance: Track model lineage, document decisions, and maintain compliance audit trails

Optimize Resource Usage: Automatically scale compute resources based on prediction demand

Integrate ML with Business Processes: Embed AI predictions into operational workflows and applications

Host Your Own LLMs: Comply with data sovereignty requirements by hosting LLMs in your preferred region.

Key Features:

• Automated MLOps Pipeline: Streamlined CI/CD for machine learning models with automated testing and deployment

• Model Registry & Versioning: Centralized repository for model artifacts with complete lineage tracking

• Multi-Environment Deployment: Seamlessly promote models from development to staging to production

• Model Monitoring UI: Track model performance, drift detection, and business impact metrics

• Data Agents: AI-powered assistants that automate data engineering and management tasks

• Transformations Library: ML models can be trained and used as data product transformations.

• LLM Self-Hosting: Host any open-source or custom language models (even LLMs) in Foundation.