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ML Ops Services
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Job Details
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Qualifications:
- 3-year diploma or higher in the related field of study, for example Computer Science or Information Technology.
Environment and Dependencies:
- Azure Databricks
- Azure DevOps (ADO)
- Microsoft Teams
Key deliverables:
- Monitoring dashboards (live and production-ready)
- Alerting frameworks and triggers
- Data quality gate definitions and implementation
- CI/CD architecture and pipeline designs
- ML project templates and reusable components
- Operational runbooks and documentation
Scope of Work:
Monitoring and observability:
- Dashboarding for model health and pipeline execution.
- Drift detection and anomaly monitoring.
- Automated alerting (email/dashboards).
Data quality and feature pipelines:
- Data quality rules (schema, null checks, thresholds).
- Feature pipeline readiness standards.
- Data contracts and SLA definitions.
CI/CD and DevOps Integration:
- CI/CD design for batch ML workloads including but not limited to explicitly enforced testing gates.
- Deployment automation and rollback strategies.
- Integration with Azure DevOps / Git repositories.
Reusable frameworks and templates:
- ML project templates (cookie-cutter) and development frameworks including model development tracking, experiment tracking, model registry, model monitoring etc.
- Standardized pipeline components for data transformation and validation.
- Scalable and standardized logging.
- Documentation and reproducibility frameworks.
Governance and operations:
- Runbooks for production support.
- PR review processes and governance.
- Knowledge transfer to internal teams.
- Compliance with secure data handling processes and privacy controls.
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