ml-pipeline-expert

Automate an end-to-end machine learning pipeline by using BigQuery for feature engineering, Python and scikit-learn for model training, and Vertex AI for deployment and monitoring. This workflow ensures reproducible and scalable MLOps operations.

The best way to deploy scikit-learn models to Vertex AI is by using an automated MLOps pipeline that trains your model with Python, saves it with joblib, and deploys it directly as a monitored endpoint on Vertex AI.

Yes, you can use BigQuery SQL for feature engineering before model training. The pipeline processes data directly in BigQuery to create robust feature sets, then passes them to Python and scikit-learn for training and evaluation.

Monitor model performance and detect data drift on Vertex AI by deploying your models through an automated MLOps pipeline. The pipeline tracks production endpoint accuracy over time to ensure sustained model reliability.

Yes, you need google-cloud-storage and google-cloud-bigquery dependencies to set up this MLOps workflow. These Google Cloud integrations are required for data processing, feature engineering, and hosting your trained models.

Use scikit-learn and pandas for model training to leverage their robust data manipulation and machine learning capabilities within a reproducible pipeline. This combination integrates seamlessly with joblib for model serialization before Vertex AI deployment.