Continuous Learning for Algorithms used in C# Digital Twins

Machine learning uses algorithms and training data to find patterns and make predictions. In a world where data can evolve rapidly, updating your machine learning algorithms to account for new data can be highly valuable.

Whether you imported an existing TensorFlow algorithm or trained an ML.NET algorithm, you can increase the accuracy of predictions over time by gathering more data points.

The following sections will describe how you can leverage the ScaleOut Digital Twins to gather new data, retrain your existing algorithms and update them without needing to redeploy your digital twin models.

• Continuous Learning with ScaleOut Digital Twins
• Gathering and Download of New Training Data
• Incremental Continuous Learning
• Manual Update of ML Algorithm