The center of the unique framework was its metadata schema, saved in Azure SQL Database, which allowed for dynamic configuration of ETL jobs. To include AI, I prolonged this schema to orchestrate machine studying duties alongside information integration, making a unified pipeline that handles each. This required including a number of new tables to the metadata repository:
- ML_Models: This desk captures particulars about every ML mannequin, together with its kind (e.g., regression, clustering), coaching datasets and inference endpoints. As an illustration, a forecasting mannequin may reference a particular Databricks pocket book and a Delta desk containing historic gross sales information.
- Feature_Engineering: Defines preprocessing steps like scaling numerical options or one-hot encoding categorical variables. By encoding these transformations in metadata, the framework automates information preparation for various ML fashions.
- Pipeline_Dependencies: Ensures duties execute within the right sequence, I.e. ETL earlier than inference, storage after inference, sustaining workflow integrity throughout levels.
- Output_Storage: Specifies locations for inference outcomes, akin to Delta tables for analytics or Azure SQL for reporting, guaranteeing outputs are readily accessible.
Think about this metadata instance for a job combining ETL and ML inference:
{
"job_id": 101,
"levels": [
{
"id": 1,
"type": "ETL",
"source": "SQL Server",
"destination": "ADLS Gen2",
"object": "customer_transactions"
},
{
"id": 2,
"type": "Inference",
"source": "ADLS Gen2",
"script": "predict_churn.py",
"output": "Delta Table"
},
{
"id": 3,
"type": "Storage",
"source": "Delta Table",
"destination": "Azure SQL",
"table": "churn_predictions"
}
]
} This schema allows ADF to handle a pipeline that extracts transaction information, runs a churn prediction mannequin in Databricks and shops the outcomes, all pushed by metadata. The advantages are twofold: it eliminates the necessity for bespoke coding for every AI use case, and it permits the system to adapt to new fashions or datasets by merely updating the metadata. This flexibility is essential for enterprises aiming to scale AI initiatives with out incurring important technical debt.
