Mental Health AI Multimodal Data Pipeline
About This Architecture
Multimodal mental health AI pipeline ingesting text and voice inputs through Auto Loader, processing via Speech-to-Text and NLP, then fusing features for crisis detection using SVM and deep learning models. Data flows through Spark Streaming and Delta Live Tables with quality checks before reaching Multimodal Fusion and Mental Health Prediction stages. The pipeline serves predictions via Gemini API with safety guardrails, outputting results to dashboards and enabling observable metrics tracking. Fork this diagram to customize feature extraction, model selection, or safety thresholds for your mental health application.
People also ask
How do you build a multimodal ML pipeline that processes both voice and text for mental health AI with crisis detection?
This diagram shows a complete pipeline ingesting user text and voice inputs through Auto Loader, processing via Speech-to-Text and NLP to extract features, then fusing multimodal data for Mental Health Prediction and Crisis Detection using SVM/deep learning models. Results flow through Gemini API with safety guardrails before dashboard output, with MLflow managing model versions and Observable Met
- Domain:
- Ml Pipeline
- Audience:
- ML engineers and data scientists building multimodal mental health AI systems
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