Machine Learning Ops
4 days ago
Work closely with Data Scientists, Data Engineers and Backend Engineers to deploy, manage, and optimize machine learning models in production environments, ensuring smooth integration and efficient operations.
- Implementing:
- Model hyper parameter optimization
- Model evaluation and explain ability
- Model training and automated retraining
- Model version tracking & governance
- Data archival & version management
- Model and drift monitoring
- Creating and using benchmarks, metrics, and monitoring to measure and improve services.
- Providing best practices and executing POC for automated and efficient model operations at scale.
- Designing and developing scalable MLOps frameworks to support models based on client requirements.
- Minimum Qualifications: Bachelor's degree of Computer Science or related field.
- Checking deployment pipelines for machine learning models.
- Collaborate with ML Engineer and Data Scientists to streamline model development and deployment process.
- Triggers CI/CD pipelines after code approvals.
- Monitors pipelines and ensures all tests pass and model artifacts are generated/stored correctly.
- Deploys updated models to prod after pipeline completion.
- Works closely with the software engineering and DevOps team to ensure smooth integration.
- Containerize models using Docker (if applicable, depending on the model) and deploy on GCP cloud platforms.
- Set up monitoring tools to track various metrics like response time, error rates, and resource utilization.
- Establish alerts and notifications to quickly detect anomalies or deviations from expected behavior.
- Analyze monitoring data, log, files, and system metrics.
- Collaborate with the data science team to develop updated pipelines to cover any faults.
- Documenting and troubleshoots, changes, and optimization.
- Work with cross-functional teams to assist in model selection and assisting in defining evaluation criteria.
- Willing to work in Banking Industry (Tangerang)
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