ML
Pipelines

Evaluate existing ML workflows, data sources, and infrastructure. Define pipeline goals, scope, and technology stack for optimal performance.

Design and implement robust data ingestion, cleaning, and transformation pipelines. Ensure data quality and availability for feature engineering.

Develop automated processes for creating, selecting, and managing features. Integrate feature stores for consistency across models.

Set up automated model training, hyperparameter tuning, and rigorous validation gates. Ensure models meet performance and fairness criteria.

Implement containerized model deployment strategies and orchestrate pipelines using tools like Kubeflow or Airflow. Establish CI/CD for ML models.

Configure continuous monitoring for pipeline health and model performance in production. Implement feedback loops for ongoing optimization and retraining.