Course Roadmap

What you'll learn in the Qdrant Operations course — from Docker deployment to distributed clusters.

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Qdrant Course Roadmap

This course teaches you to deploy and operate Qdrant — from a single Docker container to distributed clusters on Kubernetes.

Phase-by-Phase Breakdown

Phase 1: Introduction (30 minutes)

LessonWhat You'll Do
Course OverviewUnderstand Qdrant's architecture and advantages
Course RoadmapMap out the learning journey

Phase 2: Deployment Options (1.5 hours)

LessonWhat You'll Do
Docker DeploymentRun Qdrant in a single container
Docker ComposeMulti-service setup with persistence
Kubernetes with HelmProduction deployment on K8s
Lab: Deploy Qdrant three ways and compare.

Phase 3: Configuration (1 hour)

LessonWhat You'll Do
Configuration FileMaster config.yaml parameters
Storage & MemoryConfigure storage options
Performance TuningOptimize for your workload

Phase 4: Data Operations (1 hour)

LessonWhat You'll Do
Collections & PointsCreate collections, insert vectors
Vector Types & QuantizationScalar and product quantization
Payload & FilteringIndex payloads, filter queries
Lab: Build a semantic search system with payload filtering.

Phase 5: Distributed Qdrant (1.5 hours)

LessonWhat You'll Do
Clustering ConceptsUnderstand distributed architecture
Sharding StrategyConfigure sharding for your data
Replication & HASet up replicas for high availability
Scaling OperationsAdd and remove nodes
Lab: Create a 3-node Qdrant cluster.

Phase 6: Production Operations (1.5 hours)

LessonWhat You'll Do
Backup & RestoreSnapshot and recovery procedures
Monitoring & MetricsPrometheus and Grafana setup
Security & TLSEnable authentication and encryption
TroubleshootingDebug common issues
Lab: Set up monitoring and alerts for your cluster.

The Qdrant Learning Path

Qdrant Learning Path

Phase 1: Introduction

30m

LessonWhat You'll Do
Course OverviewUnderstand Qdrant's architecture and advantages
Course RoadmapMap out the learning journey
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Key Skills You'll Gain

  • Docker Deployment — Run Qdrant locally and in production
  • Kubernetes Setup — Helm charts and production patterns
  • Configuration — Tune 100+ config parameters
  • Clustering — Distribute data across multiple nodes
  • Performance — Quantization, indexing, and query optimization
  • Monitoring — Metrics, logs, and alerting

Prerequisites Checklist

Before starting hands-on labs:
  • [ ] Docker and Docker Compose installed
  • [ ] kubectl installed (for Kubernetes sections)
  • [ ] Helm 3.x installed
  • [ ] A Kubernetes cluster (kind, minikube, or cloud)
  • [ ] Python 3.8+ installed

Next Steps

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