List of Topics:
Location Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

Multimodal ML for Image, Text, and Sensor Data Fusion Using Google Cloud Vertex AI

Sensor Data

Sensor Data Fusion Using Google Cloud Vertex AI

  • Use Case : Organizations like smart manufacturing, healthcare, and autonomous vehicles generate multiple types of data—images (camera feeds), text (logs, reports), and sensor telemetry. Integrating these heterogeneous data sources into a unified ML model enables better predictions, anomaly detection, and decision-making.

Objective

  • Build a multimodal ML pipeline that fuses image, text, and sensor data.

    Perform real-time inference for critical applications (e.g., predictive maintenance, patient monitoring).

    Leverage Vertex AI to handle large-scale ML training, deployment, and model orchestration.

    Enable scalable, cloud-native, and automated ML workflows.

Project Description

  • This project implements a Google Cloud-based multimodal ML system:

    Data Ingestion: Collect images, textual logs, and sensor streams from IoT devices or applications.

    Preprocessing: Normalize images, tokenize text, and standardize sensor values.

    Fusion Model: Train a deep learning model that combines multiple modalities for enhanced prediction accuracy.

    Deployment: Serve the model for real-time or batch predictions via Vertex AI endpoints.

    Monitoring & Optimization: Track model performance, retrain as new data arrives, and adjust pipelines for latency or throughput improvements.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud Storage Stores raw multimodal datasets (images, text logs, sensor telemetry).
    Pub/Sub Ingests real-time sensor or event data streams for processing.
    Dataflow Preprocesses data streams, performs feature extraction, and prepares inputs for ML models.
    BigQuery Stores structured data (sensor readings, textual features) for analytics and historical analysis.
    Vertex AI Central platform to train, deploy, and manage multimodal ML models; supports pipelines, AutoML, and custom training.
    AI Platform Pipelines Orchestrates end-to-end ML workflows including preprocessing, training, evaluation, and deployment.
    Cloud Functions Event-driven automation (trigger model inference or retraining when new data arrives).
    Cloud Monitoring / Logging Monitors model performance, prediction latency, and system health.
    Looker / Data Studio Visualize predictions, trends, and aggregated insights from multimodal data.
    Cloud Key Management Service (KMS) Encrypts sensitive data (medical, industrial, or personal sensor data) for compliance.