High Tech — Machine Learning Model Training Workflow
FreeThis DAG orchestrates the training of machine learning models using prepared features. It ensures quality controls to mitigate bias and stores results for future reuse.
Overview
The purpose of this DAG is to manage the training of machine learning models within the high-tech sector, focusing on governance and compliance. It begins by ingesting prepared features from various data sources, including historical performance data and feature sets. The ingestion pipeline ensures that all necessary data is collected and formatted correctly for processing. The processing steps include model selection, where different algorithms are evaluated based on predefined criteria, and pe
The purpose of this DAG is to manage the training of machine learning models within the high-tech sector, focusing on governance and compliance. It begins by ingesting prepared features from various data sources, including historical performance data and feature sets. The ingestion pipeline ensures that all necessary data is collected and formatted correctly for processing. The processing steps include model selection, where different algorithms are evaluated based on predefined criteria, and performance evaluation, which assesses the models against key metrics such as accuracy and precision. Quality controls are integrated throughout the process to identify and mitigate any potential biases that may arise during training. Following the model training and evaluation, the results are stored in a centralized catalog for future reference and reuse, enabling teams to leverage successful models in subsequent projects. The DAG also incorporates monitoring mechanisms to track key performance indicators (KPIs) like model drift and performance metrics over time, ensuring that the models remain effective and compliant with industry standards. The business value of this DAG lies in its ability to streamline the model training process, enhance governance and compliance efforts, and ultimately drive better decision-making through reliable machine learning outputs.
Part of the Customer Personalization solution for the High Tech industry.
Use cases
- Improved governance through automated compliance checks
- Reduced time-to-market for machine learning solutions
- Enhanced decision-making with reliable model outputs
- Mitigated risks associated with biased model predictions
- Increased efficiency in model reuse and retraining
Technical Specifications
Inputs
- • Historical performance data
- • Prepared feature sets
- • Training datasets from compliance audits
Outputs
- • Trained machine learning models
- • Performance evaluation reports
- • Model drift monitoring dashboards
Processing Steps
- 1. Ingest prepared feature sets
- 2. Select candidate models for training
- 3. Train models using historical data
- 4. Evaluate model performance against KPIs
- 5. Implement quality controls to check for bias
- 6. Store trained models in the catalog
- 7. Monitor performance and drift over time
Additional Information
DAG ID
WK-0996
Last Updated
2025-11-08
Downloads
41