Deploy MLflow on Kubernetes
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 5. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s): - Week 5: ML Infrastructure and Experiment Tracking (slug: ml-infrastructure-105) 🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp. 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
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 5. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):
- Week 5: ML Infrastructure and Experiment Tracking (slug: ml-infrastructure-105)
🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.
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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