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Step 1 Data Acquisition & Ingestion


Multi-Source Data Capture

Spesarral (Dona/Sal)

IOT Sensor Networks

External Data (Weather API)

Cloud Object Storage
Distributed Data Feeds (Sender API)
Distributed Meteors (Imager)
Step 2 Data Processing & Buffering


1.1. Redonstratic Calibration

3. Attemptanatic Correction

3. Connection

3. Orthnorasicling Assertion

Spectral Index Calculation

Tiling

Tiling

Step 2 Telemetry Processing Pipeline


3.1 Schema Validation Clusters

2.2 Onermissing & Pilealise

5 Anomaly Detection

2.3 Temporal Enticement

2.1 Feature Registry Pendation

Centraiced Feature Store Vesisting & Cataloging

2.3 Schema Validation Clusters

2.3 Anomaly Bestirved Decision

5.5 Temporal Aggregation

2.2 Spatial Entradament

Step 5 Presentation & Human Feedback


2.1 Data Aggregation & API Services

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Step 3 Decision Intelligence & Alerting


6.1 Model Persistence

Merigantage Triggering

Extract Labeled Datasets

User Actions (Confirm/Commit/Dismiss Alerts)

6.2 Visualization Maps, Dashboards

Distributed Processing, Distributed Training Jobs, Saldatas & Domain Alerts

8.3 Ground Truth Capture DB (Annotated Data)

Step 4.1 Decision Intelligence & Alerting


6.1 CE7 Engine Monitor Multiple Streams

8.3 Alerts (Severity Levels)

8.3 Alerts (Severity Levels)

Step 6.5 Continuous Learning & MLOps


6.1 Model Performance Monitoring & Tuning

6.1 Staging API (Carry Remitted)

6.2 Interactive-Mixed Datasets

5.2 Validate & Compare Models

5.8 Validate & Compare Models

6.3 Frontend Clients

Format 6.1 Immobile Ground Truth DB (Annotated Data)

Step 1. AI Analysis & Model Inference


On-Board Inference Telemetry Model

Batch Inference (Deep Learning Models)

SIH Tech Workflow

by Sarvaish

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