Deploy AMPL within production-grade Python environments to power large-scale, multi-user optimization workflows – with commercial solvers, scalable execution, and seamless data integration.
Connect machine learning predictions directly to optimization, so your Python models don’t just forecast outcomes but decide the best action.
AMPL integrates directly into Python-based data and production systems. Python handles data, machine learning, orchestration, and deployment, while AMPL acts as the optimization engine with unified solver control.
Move structured data seamlessly between Python data workflows and AMPL models. amplpy moves structured data between pandas, NumPy, and ML libraries such as scikit-learn and AMPL models inside existing analytics pipelines.
Commercial and open-source solvers are available as Python packages, enabling scalable execution, multi-process workflows, and controlled solver environments within enterprise deployments.
Build internal optimization services, decision-support apps, AI applications, or batch jobs on cloud or on-prem infrastructure.
Built for teams operationalizing large-scale optimization across enterprise Python environments.
Multi-user and multi-instance model execution
Parallel and distributed solver execution
Unified commercial and open-source solver integration
Centralized solver control and license management
Scripted, batch, and service-based execution models
Deployment across cloud and on-prem infrastructure
Access installation guides, API references, example workflows, and applied optimization resources to support development and production deployment with amplpy.
Deploy AMPL within your enterprise Python environment — with commercial and open-source solvers available through unified execution and scalable infrastructure.
Access installation guides, API documentation, and implementation examples for deploying AMPL within Python-based analytics and production systems.
Evaluate solver performance within your Python environment. Compare commercial and open-source solvers using AMPL’s unified modeling layer to support informed deployment decisions.
