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

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, object_detection_prediction, classification_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
}