Deploy MLflow on Kubernetes
Deploy a production-ready MLflow tracking server on Kubernetes with PostgreSQL backend and S3 artifact storage. Learn to configure experiment tracking infrastructure for LLMOps.
Lab Overview
Deploy a production-ready MLflow tracking server on Kubernetes with PostgreSQL backend and S3 artifact storage. Learn to configure experiment tracking infrastructure for LLMOps.
What You'll Learn
Create S3 bucket for artifact storage using Terraform
Deploy PostgreSQL on Kubernetes as MLflow backend store
Configure and deploy MLflow tracking server
Verify MLflow installation by logging experiments
Prerequisites
Kubernetes fundamentals (deployments, services, secrets)
Basic Terraform knowledge
Understanding of persistent storage concepts
Technologies Covered
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Content & Learning
- Access to all courses and bootcamps
- Video lessons with closed captions
- Interactive quizzes and assessments
- Course completion certificates
Hands-On Labs
- Browser-based cloud labs
- Pre-configured VMs ready to use
- Playgrounds for experiments
- Multi-VM realistic scenarios
AWS Integration
- Managed AWS Account included
- Pre-configured environments
- Real-world cloud scenarios
Support & Community
- Priority support
- Active community forum
No Setup Required
- Everything runs in your browser
- No software installation needed
- Automatic environment provisioning
- Works on any device
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