ML Ops Services

Qualifications: 

• 3-year diploma or higher in the related field of study, for example Computer Science or Information Technology.

• Azure Databricks
• Azure DevOps (ADO)
• Microsoft Teams

• 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

• Dashboarding for model health and pipeline execution.
• Drift detection and anomaly monitoring.
• Automated alerting (email/dashboards).

• Data quality rules (schema, null checks, thresholds).
• Feature pipeline readiness standards.
• Data contracts and SLA definitions.

• 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.

• 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.

• Runbooks for production support.
• PR review processes and governance.
• Knowledge transfer to internal teams.
• Compliance with secure data handling processes and privacy controls.