Distributed Cloud | MLOps
Tasks
- Build automated end to end machine learning pipelines
- Convert experimental code into production ready microservices
- Implement monitoring for model performance and data drift
- Optimize cost and latency of model serving
- Package version and deploy machine learning models
- Provision scalable compute and storage with infrastructure as code
Perks/Benefits
Skills/Tech-stack
Amazon SageMaker | Apache Airflow | Azure Machine Learning | CI/CD | Data version control | Docker | Feature Store | GitHub Actions | GitLab CI | Google Vertex | Google Vertex AI | Grafana | Infrastructure as Code | Jenkins | Kubeflow | Kubernetes | MLflow | Machine Learning | Prometheus | Python | Version control | Vertex AI | “as-code”
Education
N/A
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