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

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, instance_segmentation_prediction, keypoint_detection_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
}