Summary: Streamline ML model deployment, monitoring, and lifecycle management.
Responsibilities:
- Build CI/CD pipelines for ML models using Kubeflow/MLflow.
- Optimize model performance and scalability in production.
- Implement monitoring for model drift and data quality.
Skills: - Proficiency in Docker, Kubernetes, and cloud ML services (SageMaker, Vertex AI).
- Experience with TensorFlow Serving or TorchServe.
Key Process: End-to-End ML Lifecycle Management
- Inputs: Trained models, data pipelines, deployment requirements.
- Activities:
- Build CI/CD pipelines for model deployment.
- Monitor model performance and retrain as needed.
- Manage versioning (data, models, code).
- Outputs: Deployed models, performance reports, drift alerts.
- Stakeholders: Data scientists, DevOps, business analysts.
- Tools: MLflow, Kubeflow, AWS SageMaker.