Model Monitoring Inference Aggregator

Class: ModelMonitoringInferenceAggregatorBlockV1

Source: inference.core.workflows.core_steps.sinks.roboflow.model_monitoring_inference_aggregator.v1.ModelMonitoringInferenceAggregatorBlockV1

This block 📊 transforms inference data reporting to a whole new level by periodically aggregating and sending a curated sample of predictions to Roboflow Model Monitoring.

✨ Key Features

• Effortless Aggregation: Collects and organizes predictions in-memory, ensuring only the most relevant and confident predictions are reported.

• Customizable Reporting Intervals: Choose how frequently (in seconds) data should be sent—ensuring optimal balance between granularity and resource efficiency.

• Debug-Friendly Mode: Fine-tune operations by enabling or disabling asynchronous background execution.

🔍 Why Use This Block?

This block is a game-changer for projects relying on video processing in Workflows. With its aggregation process, it identifies the most confident predictions across classes and sends them at regular intervals in small messages to Roboflow backend - ensuring that video processing performance is impacted to the least extent.

Perfect for:

• Monitoring production line performance in real-time 🏭.

• Debugging and validating your model’s performance over time ⏱️.

• Providing actionable insights from inference workflows with minimal overhead 🔧.

🚨 Limitations

• The block is should not be relied on when running Workflow in inference server or via HTTP request to Roboflow hosted platform, as the internal state is not persisted in a memory that would be accessible for all requests to the server, causing aggregation to only have a scope of single request. We will solve that problem in future releases if proven to be serious limitation for clients.

• This block do not have ability to separate aggregations for multiple videos processed by InferencePipeline - effectively aggregating data for all video feeds connected to single process running InferencePipeline.

Type identifier

Use the following identifier in step "type" field: roboflow_core/model_monitoring_inference_aggregator@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
frequency	int	Frequency of reporting (in seconds). For example, if 5 is provided, the block will report an aggregated sample of predictions every 5 seconds..	✅
unique_aggregator_key	str	Unique key used internally to track the session of inference results reporting. Must be unique for each step in your Workflow..	❌
fire_and_forget	bool	Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Model Monitoring Inference Aggregator in version v1.

• inputs: Anthropic Claude, YOLO-World Model, LMM, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Time in Zone, Keypoint Detection Model, Byte Tracker, Segment Anything 2 Model, Slack Notification, Instance Segmentation Model, Roboflow Dataset Upload, Identify Outliers, Detection Offset, Google Vision OCR, Byte Tracker, Detections Stabilizer, Detections Transformation, CSV Formatter, VLM as Detector, Google Gemini, OpenAI, SIFT Comparison, PTZ Tracking (ONVIF).md), Multi-Label Classification Model, Path Deviation, Dynamic Zone, JSON Parser, Roboflow Custom Metadata, Line Counter, Stitch OCR Detections, VLM as Classifier, Multi-Label Classification Model, Local File Sink, Velocity, Overlap Filter, VLM as Classifier, Clip Comparison, Email Notification, Keypoint Detection Model, Single-Label Classification Model, Gaze Detection, Perspective Correction, Detections Consensus, Path Deviation, Moondream2, Bounding Rectangle, OpenAI, Detections Filter, Detections Merge, Detections Stitch, Instance Segmentation Model, CogVLM, OpenAI, Twilio SMS Notification, Dynamic Crop, Detections Classes Replacement, Florence-2 Model, Template Matching, Time in Zone, Webhook Sink, SIFT Comparison, VLM as Detector, Florence-2 Model, LMM For Classification, Object Detection Model, Llama 3.2 Vision, Single-Label Classification Model, Identify Changes, OCR Model, Byte Tracker, Object Detection Model
• outputs: Blur Visualization, Triangle Visualization, Anthropic Claude, Trace Visualization, YOLO-World Model, Label Visualization, LMM, Distance Measurement, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Time in Zone, Pixel Color Count, Image Preprocessing, Keypoint Detection Model, Image Threshold, Reference Path Visualization, Segment Anything 2 Model, Slack Notification, Stability AI Outpainting, Instance Segmentation Model, Roboflow Dataset Upload, Google Vision OCR, Stability AI Inpainting, Background Color Visualization, Circle Visualization, Keypoint Visualization, Image Blur, Google Gemini, OpenAI, PTZ Tracking (ONVIF).md), Line Counter Visualization, Multi-Label Classification Model, Model Comparison Visualization, Perception Encoder Embedding Model, Dynamic Zone, Path Deviation, Roboflow Custom Metadata, Line Counter, Crop Visualization, Corner Visualization, Multi-Label Classification Model, Pixelate Visualization, Local File Sink, Cache Set, Mask Visualization, Color Visualization, Clip Comparison, Polygon Visualization, Email Notification, Keypoint Detection Model, Single-Label Classification Model, Gaze Detection, Size Measurement, Perspective Correction, Detections Consensus, Path Deviation, OpenAI, Bounding Box Visualization, Detections Stitch, Instance Segmentation Model, Twilio SMS Notification, CogVLM, OpenAI, Dynamic Crop, Detections Classes Replacement, Halo Visualization, Florence-2 Model, Dot Visualization, Template Matching, Classification Label Visualization, Webhook Sink, Time in Zone, SIFT Comparison, Stability AI Image Generation, Florence-2 Model, LMM For Classification, Object Detection Model, Ellipse Visualization, Llama 3.2 Vision, Line Counter, Single-Label Classification Model, CLIP Embedding Model, Object Detection Model, Cache Get, Polygon Zone Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Model Monitoring Inference Aggregator in version v1 has.

Bindings

• input

  • predictions (Union[classification_prediction, object_detection_prediction, keypoint_detection_prediction, instance_segmentation_prediction]): Model predictions to report to Roboflow Model Monitoring..
  • model_id (roboflow_model_id): Model ID to report to Roboflow Model Monitoring..
  • frequency (string): Frequency of reporting (in seconds). For example, if 5 is provided, the block will report an aggregated sample of predictions every 5 seconds..
  • fire_and_forget (boolean): Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..

• output

  • error_status (boolean): Boolean flag.
  • message (string): String value.

Example JSON definition of step Model Monitoring Inference Aggregator in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/model_monitoring_inference_aggregator@v1",
    "predictions": "$steps.my_step.predictions",
    "model_id": "my_project/3",
    "frequency": "3",
    "unique_aggregator_key": "session-1v73kdhfse",
    "fire_and_forget": true
}