- 🔭 End-to-End MLOps: Feature stores, model registries, automated pipelines, and serving infrastructure
- 👯 Kubernetes-Native Architecture: Multi-cloud Terraform, GitOps, service mesh integration
- 🤔 Model Lifecycle Management: Automated retraining, canary deployments, shadow testing
- ⚡ Production Monitoring: Multi-modal drift detection, intelligent alerting, auto-remediation
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Working from home
MLOps and Data Infrastructure Engineer specializing in the deployment, reliability, and governance of advanced AI systems
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Gammalance Group
- Seychelles
- [email protected]
- in/senior-mlops
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mlflow/mlflow
mlflow/mlflow PublicThe open source developer platform to build AI agents and models with confidence. Enhance your AI applications with end-to-end tracking, observability, and evaluations, all in one integrated platform.
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great-expectations/great_expectations
great-expectations/great_expectations PublicAlways know what to expect from your data.
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mlops-drift-detection
mlops-drift-detection Public⚙️ Production MLOps Pipeline. Designed and deployed a fully automated CI/CD/CT pipeline on AWS/GCP (using Docker/Kubernetes) for a domain-specific LLM.
Python 4
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