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: Detections Consensus, Path Deviation, Llama 3.2 Vision, SAM 3, Perspective Correction, Velocity, Roboflow Custom Metadata, Detections Transformation, Segment Anything 2 Model, Dynamic Crop, Identify Outliers, Dynamic Zone, Single-Label Classification Model, LMM, OpenAI, Florence-2 Model, Single-Label Classification Model, SAM 3, Time in Zone, SIFT Comparison, Moondream2, Google Gemini, Florence-2 Model, LMM For Classification, Time in Zone, Object Detection Model, OCR Model, Anthropic Claude, Google Vision OCR, VLM as Detector, Local File Sink, Keypoint Detection Model, EasyOCR, Line Counter, VLM as Detector, Email Notification, Detections Combine, Detections Filter, Byte Tracker, Overlap Filter, Gaze Detection, Roboflow Dataset Upload, Keypoint Detection Model, Slack Notification, Multi-Label Classification Model, Object Detection Model, Detections Stabilizer, Google Gemini, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Multi-Label Classification Model, Detections Merge, Twilio SMS Notification, Byte Tracker, Instance Segmentation Model, Seg Preview, VLM as Classifier, CSV Formatter, Motion Detection, OpenAI, Byte Tracker, Webhook Sink, PTZ Tracking (ONVIF).md), Detections Classes Replacement, Instance Segmentation Model, Detections Stitch, YOLO-World Model, Stitch OCR Detections, JSON Parser, Clip Comparison, CogVLM, Identify Changes, Template Matching, Path Deviation, Email Notification, VLM as Classifier, OpenAI, Bounding Rectangle, SAM 3, Anthropic Claude, Time in Zone, Detection Offset, SIFT Comparison, OpenAI
• outputs: Llama 3.2 Vision, SAM 3, Polygon Zone Visualization, Pixelate Visualization, Distance Measurement, Trace Visualization, Roboflow Custom Metadata, Image Threshold, Icon Visualization, Stability AI Outpainting, Dynamic Zone, Model Comparison Visualization, Single-Label Classification Model, Cache Get, Size Measurement, Florence-2 Model, Single-Label Classification Model, SAM 3, SIFT Comparison, Moondream2, Florence-2 Model, LMM For Classification, Anthropic Claude, Image Blur, Stability AI Image Generation, Local File Sink, Keypoint Detection Model, Gaze Detection, Background Color Visualization, Keypoint Detection Model, Multi-Label Classification Model, Google Gemini, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Instance Segmentation Model, Motion Detection, Morphological Transformation, OpenAI, YOLO-World Model, Clip Comparison, CogVLM, Path Deviation, CLIP Embedding Model, Crop Visualization, SAM 3, Anthropic Claude, Time in Zone, OpenAI, Line Counter, Detections Consensus, Path Deviation, Blur Visualization, Perception Encoder Embedding Model, Perspective Correction, Bounding Box Visualization, QR Code Generator, Segment Anything 2 Model, Polygon Visualization, Dynamic Crop, LMM, OpenAI, Classification Label Visualization, Mask Visualization, Time in Zone, Google Gemini, Circle Visualization, Time in Zone, Ellipse Visualization, Object Detection Model, Image Preprocessing, Color Visualization, Google Vision OCR, Keypoint Visualization, Email Notification, Line Counter, Roboflow Dataset Upload, Triangle Visualization, Slack Notification, Object Detection Model, Halo Visualization, Corner Visualization, Dot Visualization, Multi-Label Classification Model, Twilio SMS Notification, Seg Preview, Reference Path Visualization, Webhook Sink, PTZ Tracking (ONVIF).md), Detections Classes Replacement, Instance Segmentation Model, Detections Stitch, Contrast Equalization, Stitch OCR Detections, Stability AI Inpainting, Line Counter Visualization, Cache Set, Template Matching, Email Notification, OpenAI, Label Visualization, Pixel Color Count

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[instance_segmentation_prediction, keypoint_detection_prediction, object_detection_prediction, classification_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
}