MLOps Pipeline
Kubernetes + Kubeflow + MLflow
Built end-to-end MLOps pipeline automating ML lifecycle from training to deployment. Orchestrates distributed training jobs using Kubeflow Pipelines on local Kubernetes cluster, manages experiment tracking with MLflow for model versioning and lineage, implements automated deployment workflows with KServe for model serving. Integrated OpenTelemetry for comprehensive observability, Prometheus for metrics, and Grafana for visualization dashboards.
The Challenge
Data science teams at enterprises struggle with production ML deployment: 87% of models never reach production due to infrastructure complexity, lack of collaboration tools, manual deployment processes taking 6-12 weeks, inability to monitor model performance post-deployment leading to silent failures costing millions in revenue. Fragmented tooling across training (Jupyter notebooks), serving (custom Flask apps), monitoring (manual dashboards) creates operational nightmares.
The Solution
Platform reduces model deployment time from 8 weeks to 2 hours through self-service workflows. Data scientists submit training jobs via CLI/UI → Platform handles infrastructure provisioning, distributed training, hyperparameter optimization, validation, deployment, monitoring. Shadow deployments allow safe testing in production traffic before full rollout. Automated drift detection catches data quality issues before impacting business metrics.
Technology Stack
Architecture
- Kubernetes-native operators with custom CRDs
- Distributed training with Ray Tune hyperparameter optimization
- Real-time feature store with Redis backend
- MLflow for experiment tracking and model registry
- KServe for production model serving
- Istio service mesh for traffic management
- Automated CI/CD with GitLab and ArgoCD
- Model drift detection with Evidently AI
- A/B testing framework with canary deployments
- Multi-cloud architecture with unified control plane
- Federated learning for privacy-preserving training
- Comprehensive observability (Prometheus, Grafana, Jaeger, ELK)
Key Features
Results & Impact
- Deployed 10 PyTorch models in local K8s cluster with automated CI/CD
- Implemented experiment tracking managing 50+ training runs via MLflow
- Achieved 40% faster deployment via automated Kubeflow pipelines
- Built comprehensive observability with OpenTelemetry + Prometheus + Grafana
- Integrated KServe for model serving with load testing (1K simulated requests)
- Created reusable ML pipeline templates for training, validation, and deployment