Course Roadmap

This course is not a "what is a vector database" introduction — the internet has plenty of those. It's a hands-on operations guide: you deploy real Milvus instances, configure them correctly, scale them, secure them, and keep them running.

The interactive walkthrough below shows exactly what you'll be doing across each phase.

What This Course Is Not

• A deep-dive into embedding models or vector similarity algorithms
• A machine learning or data science course
• Theory-heavy documentation without practical exercises

Every lesson follows a lab pattern: download config files, run commands, observe the result, understand what happened.

Prerequisites

You'll get the most out of this course if you're comfortable with:

• Linux command line — navigate, edit files, run commands
• Docker and Docker Compose — pull images, run containers
• YAML — read and write configuration files
• Kubernetes basics — pods, services, Helm (for distributed sections)

Phase-by-Phase Breakdown

Phase 1: Introduction

Duration: 30 minutes

• Course overview and learning objectives
• Understanding the Milvus architecture
• Choosing your deployment mode

Phase 2: Deployment Options

Duration: 2 hours

Phase 3: Dependencies Deep Dive

Duration: 1 hour 30 minutes Learn the external services Milvus depends on:

• etcd: Metadata storage — backup, restore, and cluster sizing
• MinIO/S3: Object storage — configuration, bucket policies, performance
• Pulsar/Kafka: Message queue — topic sizing, retention, failover

Phase 4: Configuration Mastery

Duration: 1 hour 30 minutes Demystify milvus.yaml:

• Proxy configuration for connection handling
• Coordinator tuning for your workload
• Segment management for optimal performance

Phase 5: Data Operations

Duration: 1 hour 30 minutes Design decisions that affect performance:

• Collection schemas and field types
• Partitioning for multi-tenant isolation
• Index selection guide (HNSW vs IVF vs DISKANN)

Phase 6: Scaling & Cluster Operations

Duration: 1 hour 30 minutes Grow your cluster:

• Add Query Nodes for read scaling
• Add Data Nodes for write throughput
• Rolling upgrades without downtime

Phase 7: Performance Optimization

Duration: 1 hour Tune for your workload:

• Query optimization techniques
• Memory management with MMAP
• Compaction tuning for write-heavy loads

Phase 8: Security

Duration: 45 minutes Protect your data:

• Enable authentication and RBAC
• Configure TLS for all connections

Phase 9: Backup & Recovery

Duration: 1 hour Prepare for disasters:

• Backup metadata (etcd) and data (MinIO)
• Cross-cluster migration procedures
• Point-in-time recovery

Phase 10: Monitoring & Troubleshooting

Duration: 1 hour Keep it running:

• Key metrics and Grafana dashboards
• Common issues and resolution steps
• Log analysis techniques

The Lab Pattern

Every hands-on section follows this structure:

1. 1 Setup — Get the configuration files from GitHub
2. 2 Deploy — Run the commands to start services
3. 3 Verify — Check that everything is healthy
4. 4 Experiment — Run test workloads
5. 5 Observe — Check logs, metrics, and behavior

GitHub Repository

All configuration files, Docker Compose files, and scripts are available at:

github.com/VibhuviOiO/milvus-ops Each lesson links to the specific directory you need.

Next Steps

Ready to start deploying? Choose your path:

• Milvus Lite — If you want to start coding immediately
• Standalone — If you want a simple production setup
• Docker Compose — If you want the full stack locally