Use this example context file to accelerate data science workflows with Maia, Matillion’s agentic AI assistant. It demonstrates how your data science team can integrate Matillion’s data preparation capabilities with AI-driven automation to:
- Engineer and serve high-quality features.
- Monitor model performance in production.
- Automate retraining pipelines.
- Maintain robust, versioned ML workflows.
What’s inside the context file?
This sample file includes:
- Sample telemetry and model performance datasets.
- AI-triggered orchestration jobs in Matillion.
- Best practices for:
- Feature engineering and optimization.
- Data quality checks.
- Model versioning and retraining logic.
Key Maia Enhancements for Data Scientists
- Feature Delivery: Automatically curate and deliver pre-processed, feature-engineered datasets from Matillion for downstream model training.
- Model Monitoring and Retraining: Continuously monitor performance metrics and trigger Matillion jobs to retrain or recalibrate your models when thresholds are crossed.
- Data Exploration with AI: Use natural language prompts to detect data anomalies, explore distributions, or compare transformations—all within your Matillion-prepared datasets.
- Transformation Suggestions: Get AI-recommended features or data prep steps based on early model experiments and dataset profiling.
How to use
- Download the sample file below.
- Replace placeholder metadata, datasets, and job names with your own project-specific standards.
- Save it to your project’s
.matillion/maia/rules/directory. - Maia will automatically apply these practices when generating pipeline logic
🔗 Learn how to use context files in Maia
📥 Download
Download the sample context file
Use this as a template to build your own ML-ready data preparation workflows in Matillion.