AI Control Tower
AI Control Tower is a robust, secure suite of monitoring process activities that work with predictive AI to improve workflows with dynamic, predictive capabilities. These predictive AI agents enable real-time monitoring, control, and adaptation for process instances, dynamically adjusting them based on AI-driven predictions. AI Control Tower seamlessly handles AI agent integration and orchestration across multiple apps. AI Control Tower also provides centralized, real-time monitoring and analytics features that leverage AI to help organizations oversee their operations, improve governance, and optimize their business processes.
AI Control Tower integrates with third-party AI prediction models such as Amazon SageMaker and Microsoft Azure Machine Learning to create, retrieve, and respond to predictions.
Amazon SageMaker
- Get Image Classification - An activity that creates classifications for an image uploaded as input to Amazon SageMaker. The image classification data represents labels or categories associated with the image you can use in your AgilePoint NX app..
- Get Prediction - An activity that retrieves a prediction from an Amazon SageMaker model.
- Image Classification - Adapt Flow - An activity that creates classifications for an image uploaded as input to Amazon SageMaker. The image classification data represents labels or categories associated with the image you can use in your AgilePoint NX app. If conditions configured in the AI model occur in the prediction, it can roll back or roll forward a process to a different activity.
- Image Classification - Initiate Subprocess - An activity that creates classifications for an image uploaded as input to Amazon SageMaker. The image classification data represents labels or categories associated with the image you can use in your AgilePoint NX app. If conditions configured in the AI model occur in the prediction, it can start a subprocess.
- Image Classification - Send Notification - An activity that creates classifications for an image uploaded as input to Amazon SageMaker. The image classification data represents labels or categories associated with the image you can use in your AgilePoint NX app. If conditions configured in the AI model occur in the prediction, it can send an email notification.
- Prediction - Adapt Flow - An activity that creates predictions created from data provided from an AgilePoint NX app to Amazon SageMaker. If conditions configured in the AI model occur in the prediction, it can roll back or roll forward a process to a different activity.
- Prediction - Initiate Subprocess - An activity that creates predictions created from data provided from an AgilePoint NX app to Amazon SageMaker. If conditions configured in the AI model occur in the prediction, it can start a subprocess.
- Prediction - Send Notification - An activity that creates predictions created from data provided from an AgilePoint NX app to Amazon SageMaker. If conditions configured in the AI model occur in the prediction, it can send an email notification.
Microsoft Azure Machine Learning
- Get Prediction - An activity that retrieves a prediction from a Microsoft Azure Machine Learning model.
- Prediction - Adapt Flow - An activity that creates predictions created from data provided from an AgilePoint NX app to Microsoft Azure Machine Learning. If conditions configured in the AI model occur in the prediction, it can roll back or roll forward a process to a different activity.
- Prediction - Initiate Subprocess - An activity that creates predictions created from data provided from an AgilePoint NX app to Microsoft Azure Machine Learning. If conditions configured in the AI model occur in the prediction, it can start a subprocess.
- Prediction - Send Notification - An activity that creates predictions created from data provided from an AgilePoint NX app to Microsoft Azure Machine Learning. If conditions configured in the AI model occur in the prediction, it can send an email notification.
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Keywords
AI Control Tower, AI, predictive AI, adaptive AI, artificial intelligence, machine learning