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Streamlining the connection to trained ML models with the ONNX Helper Library


Streamlining the connection to trained ML models with the ONNX Helper Library

There are many cases where it’s desirable to incorporate trained machine learning (ML) models into a simulation model. Now, thanks to a new AnyLogic library, using ONNX ML models is easier and more efficient.

By incorporating this add-on library into your AnyLogic environment, your models can access its functionalities, just like with any other built-in library. It’s simply a matter of adding the helper object to your model and configuring it. Read on to find out more.

Scaling AnyLogic Models for Mass Runs and Sub-Second Responses


Scaling AnyLogic Models for Mass Runs and Sub-Second Responses

More runs, more users, and more model. More and more is demanded of simulation models as clients analyze greater numbers of scenarios, expand user access, and model in greater detail. How to scale and meet these demands?

PwC emerging technology senior manager Sindy Ma is an expert on scaling simulations. Her presentation at the AnyLogic Conference 2021 showed three real-world case studies of projects that met the challenge of scaling simulations for more scenarios, more users, and more model. Find out more...

Don’t try deep reinforcement learning without this


Don’t try deep reinforcement learning without this

Automated systems have reached their limits and companies wanting to further enhance business processes are turning to artificial intelligence (AI) technologies like machine learning (ML). In an AnyLogic workshop, Microsoft Autonomous Systems Principal Program Manager Kence Anderson explored the advanced decision-making possibilities of ML and showed how Microsoft’s machine teaching concept is achieving faster training times.

The easily accessible workshop session provides a high-level overview of AI’s state of the art with examples from Microsoft and DeepMind research, as well as illustrative Karate Kid analogies.