DeepSim takes reinforcement learning out of the lab and into the real world. It unites open-source and proprietary algorithms to deliver a powerful deep-learning engine that automatically generates superior controller software for everything from electromechanical systems to complex factory processes.
DeepSim is the engine that powers minds.ai Maestro and minds.ai Flow. It’s an inherently flexible platform that combines the planet’s most powerful open-source AI tools like Ray, MLflow, TensorFlow, PyTorch, Numpy, Pandas, Python, and Apache Airflow into a simple—and stable—cloud-based enterprise solution.
“Developing on Azure with the minds.ai platform was eye-openingly simple. I was completely blown away that one week into the project we almost had a minimum viable product.”
Sven Jesper Knudsen
Chief Specialist and Modeling & Analytics Module Design Owner
Every DeepSim solution is purpose-built by a team of leading AI architects, data scientists, and engineers. Our team works closely with customers to support the development and implementation of optimization frameworks.
With DeepSim, it’s possible to optimize complex enterprise-scale processes within your secure environment: on-prem, in the cloud, or both. And since DeepSim scales compute resources based on the training scenario, it eliminates idle time charges, further reducing optimization or operation expenses.
DeepSim harnesses a neural network to intelligently allocate compute resources based on whatever requirements are specified by the user: time-to-solution, the maximum number of simulator licenses, compute budget, and more.