Data Engineering for Industrial AI: Building Data Pipelines from Factory to Model
Learn to build data pipelines for industrial AI, data collection from PLCs, time series databases, ETL processes, and data quality for manufacturing.
Industrial AI is only as good as its data. Before any machine learning model can be trained, clean, structured data must be collected, stored, and processed from factory systems.
Industrial Data Pipeline
- -Data collection, PLC data via OPC UA, sensor data via MQTT
- -Edge processing, Filtering, aggregation, compression
- -Storage, Time series databases (InfluxDB, TimescaleDB)
- -ETL, Data transformation and feature engineering
- -Model training, ML/DL model development
- -Visualization, Grafana, Power BI
EDWartens bridges Physical AI (data sources) and Digital AI (analytics) in our data engineering curriculum.
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