Vector Databases Complete Course: Pinecone, Qdrant, Weaviate, Milvus & pgvector
Learn vector databases from fundamentals to production. Master Pinecone, Qdrant, Weaviate, Milvus, and pgvector. 10 modules, 4 projects, 35+ hours. Build semantic search, RAG, and recommendation systems.
Course Snapshot
| Course Code | AIM-604 |
| Duration | 35+ hours |
| Modules | 10 |
| Hands-on Projects | 4 |
| Skill Level | Intermediate to Advanced |
| Databases Covered | Pinecone, Qdrant, Weaviate, Milvus, pgvector |
| Index Types Covered | HNSW, IVF, PQ, Flat, ScaNN |
| Use Cases | Semantic search, RAG, recommendations |
| Certificate | Yes — Vector Databases Complete |
| Price | ₹3,999 (Early Bird ₹2,499) |
| Last Updated | July 2026 |
Course Overview
Vector databases are the storage layer that powers modern AI applications. Every semantic search engine, every RAG system, every recommendation engine, and every AI agent with memory relies on vector databases to find meaning in data at scale. This course takes you from the mathematical foundations of vector similarity through the practical mastery of five major vector databases: Pinecone, Qdrant, Weaviate, Milvus, and pgvector. You will understand what happens inside a vector index — how HNSW, IVF, and product quantization actually work — without drowning in academic papers. Then you will build real systems: a semantic search engine that finds relevant documents by meaning not keywords, a RAG retrieval layer that powers grounded LLM applications, and a recommendation system that suggests content based on behavioral and content similarity. We cover the full lifecycle from embedding generation and index creation through query optimization, filtering, multi-tenancy, and production deployment. You will learn the trade-offs between managed and self-hosted databases, when to use a dedicated vector database versus pgvector in your existing PostgreSQL, and how to benchmark and choose the right database for your workload. By the end of this course, you will have shipped four production-grade projects, a deep understanding of vector database internals, and the confidence to architect retrieval systems that scale from thousands to billions of vectors without sacrificing speed or accuracy.
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Enroll Now — Limited Seats →Who This Course Is Built For
This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
Backend & Platform Engineers
You build data infrastructure and need to add semantic search, similarity matching, or vector-based retrieval to your systems. You need to choose, configure, and scale a vector database.
AI & ML Engineers Building RAG Systems
You build RAG pipelines and need to go beyond basic vector store usage. You want to understand indexing, filtering, and performance tuning to build retrieval that scales.
Data Engineers & Architects
You design data platforms and need to evaluate vector databases for enterprise workloads. You need to compare options, benchmark performance, and plan deployment architecture.
Developers Building Search & Recommendations
You build search or recommendation features and want to move beyond keyword matching to semantic similarity. You need practical, hands-on guidance with real databases.
This Course Is NOT For You If:
- Beginners with no database or programming experience — you need intermediate Python and basic SQL knowledge.
- Those wanting pure ML model training — this course focuses on the storage and retrieval layer, not model training.
- Researchers looking for novel index algorithm development — we use existing algorithms, not invent new ones.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to explain how vector embeddings capture semantic meaning and choose the right embedding model for your use case.
- 2 You will be able to configure and optimize HNSW, IVF, and product quantization indexes for optimal recall-latency trade-offs.
- 3 You will be able to build production semantic search systems with Pinecone, including namespaces, metadata filtering, and sparse-dense hybrid search.
- 4 You will be able to deploy and manage Qdrant for self-hosted vector search with payload filtering, quantization, and distributed collections.
- 5 You will be able to use Weaviate and Milvus for large-scale vector search with their unique features and GraphQL/gRPC interfaces.
- 6 You will be able to integrate pgvector into PostgreSQL for in-database vector search with hybrid SQL-vector queries.
- 7 You will be able to build a RAG retrieval layer that combines vector search with metadata filtering, reranking, and multi-vector retrieval.
- 8 You will be able to design and implement a recommendation system using content-based and collaborative filtering with vector similarity.
- 9 You will be able to benchmark vector databases on recall, latency, throughput, and cost to make data-driven database choices.
- 10 You will be able to deploy vector databases to production with monitoring, scaling, backup, and disaster recovery strategies.
Download Free Course Syllabus
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Download Free Syllabus →Complete Course Curriculum
10 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — Vector Database Foundations
3 hours- The shift from keyword search to semantic search and why traditional databases cannot do vector similarity.
- Real-world use cases: semantic search, RAG, recommendations, deduplication, and anomaly detection.
- The vector database landscape: managed vs self-hosted, dedicated vs embedded, and how to choose.
- How embedding models convert text, images, and audio into high-dimensional vectors.
- Choosing embedding models: OpenAI, Cohere, BGE, E5, and the trade-offs of each.
- Dimensionality, storage cost, and the relationship between embedding size and retrieval quality.
- How cosine similarity, dot product, and L2 distance measure vector similarity differently.
- When to use each metric based on your embedding model and normalization requirements.
- Why most text embedding models use cosine similarity and what happens when you use the wrong metric.
- Why exact search (brute force) does not scale beyond a few hundred thousand vectors.
- How approximate nearest neighbor (ANN) search trades a small accuracy loss for massive speed gains.
- The recall-latency trade-off and how to tune it for your application's requirements.
MODULE 2 — Pinecone Mastery
4 hours- Pinecone's managed architecture: pods, indexes, collections, and namespaces.
- Creating your first index: choosing dimensionality, metric, and pod type.
- Understanding Pinecone's pricing model and estimating costs for your workload.
- Batching upserts for efficient ingestion of large datasets.
- Metadata filtering: combining vector similarity with structured filters for precise retrieval.
- Querying with namespace isolation for multi-tenant applications.
- Combining dense vector search with sparse keyword vectors for hybrid retrieval.
- Using Pinecone's sparse-dense vectors to get the best of semantic and keyword search.
- Tuning the alpha parameter to balance sparse and dense contributions.
- Choosing between starter, standard, and enterprise pods based on collection size.
- Collection backups, index cloning, and disaster recovery strategies.
- Monitoring query latency, upsert throughput, and index size in production.
MODULE 3 — Qdrant Mastery
4 hours- Qdrant's Rust-based architecture: collections, points, payloads, and segments.
- Running Qdrant locally with Docker and deploying to production with Docker Compose.
- Choosing between self-hosted Qdrant and Qdrant Cloud based on your needs.
- Using Qdrant's payload filtering to combine vector search with structured conditions.
- Creating payload indexes for faster filtering on frequently queried fields.
- Building complex filter conditions with nested logic and geo queries.
- Scalar quantization and product quantization for reducing memory usage by 4-32x.
- Configuring HNSW parameters: m, ef_construct, and ef for your recall-latency target.
- Benchmarking Qdrant on your dataset and tuning for optimal performance.
- Horizontal scaling with sharding and replication for large collections.
- Setting up Qdrant clusters with multiple nodes and consistent hashing.
- Monitoring, backup, and recovery for production Qdrant deployments.
MODULE 4 — Weaviate & Milvus
4 hours- Weaviate's schema-based architecture: classes, properties, and vectorization modules.
- Using Weaviate's built-in vectorizer modules for automatic embedding generation.
- Querying with Weaviate's GraphQL API for vector search, hybrid search, and BM25.
- Multi-tenancy in Weaviate for isolating data per customer or tenant.
- Cross-references between objects for graph-like relationships alongside vector search.
- Generative modules: using Weaviate's built-in generative search for RAG without external orchestration.
- Milvus's cloud-native architecture: proxy, query nodes, data nodes, and etcd.
- Deploying Milvus standalone for development and distributed mode for production.
- Choosing index types in Milvus: HNSW, IVF_FLAT, IVF_SQ8, and IVF_PQ.
- Partitioning and sharding strategies for billion-scale vector collections.
- Using Milvus's gRPC API for high-throughput ingestion and querying.
- Monitoring Milvus clusters with Prometheus and Grafana for production observability.
MODULE 5 — pgvector
3 hours- Installing the pgvector extension in PostgreSQL and creating vector columns.
- Choosing between HNSW and IVFFlat indexes in pgvector and their trade-offs.
- Tuning index parameters: m, ef_construction for HNSW and lists, probes for IVFFlat.
- Combining vector similarity search with SQL WHERE clauses for metadata-aware retrieval.
- Using ORDER BY with vector distance operators for ranked results.
- Joining vector search results with relational tables for enriched responses.
- When pgvector outperforms dedicated vector databases and when it does not.
- Optimizing PostgreSQL for vector workloads: memory, shared buffers, and parallel queries.
- Scaling pgvector with read replicas, partitioning, and connection pooling.
- Building a RAG retrieval layer with pgvector that leverages your existing PostgreSQL data.
- Using pgvector with Django ORM, SQLAlchemy, and Prisma for application integration.
- Migrating from pgvector to a dedicated vector database when you outgrow it.
MODULE 6 — Semantic Search Systems
4 hours- Designing a search architecture: query embedding, vector search, reranking, and result formatting.
- Handling search UX: pagination, faceted filtering, and result highlighting.
- Benchmarking search quality with relevance metrics and user feedback.
- Why pure vector search misses exact-match queries and how hybrid search fixes it.
- Implementing hybrid search with reciprocal rank fusion and weighted score combination.
- Tuning the balance between keyword and vector search for your corpus.
- Using cross-encoder rerankers to boost top-k precision after vector retrieval.
- Integrating Cohere Rerank, BGE-Reranker, and custom reranking models.
- Measuring the precision improvement from reranking and its latency cost.
- Building a relevance evaluation pipeline with labeled queries and expected results.
- Using nDCG, MRR, and recall@k to measure and compare search configurations.
- Iteratively improving search quality through embedding, chunking, and parameter tuning.
MODULE 7 — Vector Databases for RAG
4 hours- Designing the retrieval layer that connects your vector database to the LLM generation stage.
- Choosing chunk size and overlap based on your vector database and embedding model.
- Storing chunk metadata: source document, page number, section, and confidence scores.
- Storing multiple vector representations per document for different retrieval strategies.
- Parent-child retrieval: retrieving small chunks but returning the parent document for context.
- Using dense and sparse vectors together for hybrid RAG retrieval.
- Using metadata filters to restrict retrieval to specific documents, dates, or categories.
- Building self-querying retrieval where the LLM generates filters from natural language.
- Handling multi-tenant RAG with namespace or partition isolation in each database.
- Caching embeddings and query results to reduce latency and API costs.
- Tuning top-k and similarity thresholds for optimal context window usage.
- Monitoring retrieval quality in production and identifying drift over time.
MODULE 8 — Recommendation Systems
3 hours- Embedding items (products, articles, videos) into vectors for similarity-based recommendations.
- Building a "similar items" feature using vector search in your chosen database.
- Handling cold-start problems for new items with no interaction history.
- Learning user and item embeddings from interaction data using matrix factorization.
- Storing user vectors in your vector database for real-time personalized recommendations.
- Combining content-based and collaborative filtering signals for hybrid recommendations.
- Building a real-time recommendation API with vector search and business rule filtering.
- Handling diversity, novelty, and serendipity in recommendation results.
- A/B testing recommendation strategies and measuring click-through and engagement.
- Handling millions of users and items with sharded vector databases.
- Updating user vectors in real-time as new interactions arrive.
- Balancing exploration and exploitation with multi-armed bandit approaches.
MODULE 9 — Production & Performance
3 hours- Designing fair benchmarks: dataset selection, query distribution, and recall measurement.
- Benchmarking tools: ann-benchmarks, custom scripts, and vendor-provided benchmarks.
- Interpreting benchmark results and avoiding common pitfalls and vendor marketing.
- Sharding strategies: hash-based, range-based, and semantic sharding for large collections.
- Memory optimization: quantization, disk-based indexes, and tiered storage.
- When to move from a single node to a distributed cluster and the operational complexity it adds.
- Key metrics to monitor: query latency, upsert throughput, recall, and memory usage.
- Setting up dashboards with Prometheus, Grafana, and vendor-specific monitoring tools.
- Alerting on performance degradation, index corruption, and capacity thresholds.
- Backup strategies for each database: snapshots, collections, and incremental backups.
- Migrating between vector databases with minimal downtime and data integrity checks.
- Designing for disaster recovery with multi-region replication and failover procedures.
MODULE 10 — Capstone Projects
3 hours- Build a semantic search engine over 10,000+ documents using Pinecone or Qdrant.
- Implement hybrid search, metadata filtering, and reranking for high-precision results.
- Deploy with a web UI and a relevance evaluation dashboard.
- Build a production RAG retrieval layer with pgvector or Qdrant for a 50,000-chunk corpus.
- Implement multi-vector retrieval, metadata filtering, and semantic caching.
- Integrate with an LLM generation stage and evaluate end-to-end RAG quality.
- Build a content-based and collaborative filtering recommendation system with vector search.
- Implement real-time user vector updates and business rule filtering.
- Deploy with an API and measure recommendation diversity and coverage.
- Benchmark Pinecone, Qdrant, Weaviate, and pgvector on a standardized dataset.
- Measure recall, latency, throughput, and cost across different workloads.
- Write a technical report with recommendations for different use cases.
Real-World Projects & Portfolio Outcomes
4 portfolio-worthy projects that prove your skills to employers.
Semantic Search Engine
Build a semantic search engine over 10,000+ documents with hybrid search, metadata filtering, reranking, and a relevance evaluation dashboard.
RAG Retrieval Layer
Build a production RAG retrieval layer with multi-vector retrieval, metadata filtering, semantic caching, and LLM integration for a 50,000-chunk corpus.
Recommendation System
Build a content-based and collaborative filtering recommendation system with real-time user vector updates, business rule filtering, and diversity metrics.
Multi-Database Benchmark
Benchmark Pinecone, Qdrant, Weaviate, and pgvector on a standardized dataset measuring recall, latency, throughput, and cost across different workloads.
Tools, Technologies & Models Covered
LLM Models Covered
| text-embedding-3-large | OpenAI | High-quality embeddings for production search and RAG |
|---|---|---|
| text-embedding-3-small | OpenAI | Cost-effective embeddings for high-volume systems |
| Cohere embed v3 | Cohere | Multilingual embeddings with enterprise support |
| BGE-large-en-v1.5 | open-source | Self-hosted embeddings, no API costs, high quality |
| E5-large-v2 | open-source | Strong retrieval performance, self-hostable |
| Cohere Rerank 3 | Cohere | Cross-encoder reranking for precision boost |
| BGE-Reranker-large | open-source | Self-hosted reranking model |
| all-MiniLM-L6-v2 | open-source | Fast, lightweight embeddings for prototyping |
Tools & Frameworks Covered
| Pinecone | Managed vector database for production search and RAG | Deep — all features, namespaces, hybrid search |
|---|---|---|
| Qdrant | Self-hosted or managed vector database with payload filtering | Deep — quantization, distributed, filtering |
| Weaviate | Schema-based vector database with built-in vectorizers | Intermediate — GraphQL, multi-tenancy, generative |
| Milvus | Cloud-native vector database for billion-scale collections | Intermediate — distributed, sharding, gRPC |
| pgvector | PostgreSQL extension for in-database vector search | Deep — hybrid queries, indexing, scaling |
| FAISS | Facebook's library for efficient similarity search | Intermediate — index building, GPU support |
| HNSWLib | Lightweight HNSW library for local prototyping | Intermediate — fast local search |
| LangChain Vector Stores | Unified interface across multiple vector databases | Intermediate — abstraction layer |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Rajesh Kumar
Distributed Systems Engineer & Former Vector Database Team Lead at a top search infrastructure company
12+ years in distributed databases and search infrastructure, 3+ years specifically in vector databases
Built vector search systems handling billions of vectors for e-commerce, content, and enterprise search
Trained 4,000+ engineers in vector database architecture and deployment
The right vector database is the one that fits your scale, your team, and your budget. Do not chase the newest tool — benchmark, measure, and choose based on data, not hype.
Course Team & Curriculum Design
This course is co-developed with engineers who have built and operated vector databases at scale. Our Pinecone module is reviewed by a Pinecone solutions architect, our Qdrant module by a contributor to the Qdrant open-source project, our Milvus module by a Milvus community member, and our pgvector module by a PostgreSQL performance specialist.
Learning Path & Prerequisites
Prerequisites
- Intermediate Python programming — you should be comfortable with async, classes, and API calls.
- Basic SQL knowledge — familiarity with PostgreSQL is helpful for the pgvector module.
- Understanding of REST APIs and Docker for running self-hosted databases locally.
- Familiarity with embeddings and LLMs — completion of Prompt Engineering Pro or equivalent is recommended but not required.
Recommended Learning Paths
Vector Database Engineer: From Zero to Production
- Start with Module 1 to understand vector database fundamentals, embeddings, and similarity metrics.
- Complete Module 2 on Pinecone to learn the managed vector database workflow end to end.
- Study Module 3 on Qdrant for self-hosted deployment skills and deeper index control.
- Explore Modules 4 and 5 to understand Weaviate, Milvus, and pgvector for informed database selection.
- Apply your knowledge in Modules 6-8 to build search, RAG, and recommendation systems.
- Finish with Module 9 for production skills and Module 10 for capstone projects.
RAG Engineer Adding Vector Database Skills
- Review Module 1 if you are new to vector database internals and similarity metrics.
- Focus on Module 7 for vector databases in RAG — this is your core use case.
- Complete either Module 2 (Pinecone) or Module 5 (pgvector) depending on your infrastructure.
- Study Module 6 for semantic search patterns that apply to RAG retrieval.
- Use Module 9 for production deployment and monitoring of your RAG vector store.
- Complete Project 2 (RAG Retrieval Layer) as your capstone.
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Early Bird | ₹2,499 | Full course access, 4 project reviews, community access, certificate — limited time offer |
|---|---|---|
| Standard | ₹3,999 | Full course access, 4 project reviews, community access, certificate, 3 months instructor Q&A |
| Premium | ₹6,999 | Everything in Standard plus 1-on-1 mentorship, career guidance, lifetime updates, priority support |
What Is Included
- 35+ hours of HD video lessons with downloadable transcripts
- 10 modules with hands-on labs and database-specific exercises
- 4 capstone projects with personalized feedback
- Access to our private community of vector database engineers
- Downloadable code templates for all five databases
- Benchmark suite for comparing vector databases
- Certificate of completion — Vector Databases Complete
- 3 months of instructor Q&A access (Standard and Premium)
- Lifetime course updates as databases and techniques evolve
30-day money-back guarantee. If you complete the first 3 modules and do not find the course valuable, we will refund your full payment — no questions asked.
Frequently Asked Questions
Which vector database should I learn first?
We recommend starting with Pinecone (Module 2) because it is fully managed and lets you focus on concepts without infrastructure setup. Then learn Qdrant (Module 3) for self-hosted skills. By the end, you will know all five databases and can choose based on your needs.
Do I need to know machine learning or linear algebra?
No. We explain vector similarity and indexing concepts intuitively without heavy math. You need intermediate Python and basic database knowledge. The course is applied and practical, not theoretical.
Will I need to pay for managed vector databases during the course?
Pinecone and Qdrant Cloud have free tiers sufficient for the course. Weaviate Cloud also has a free sandbox. For Milvus and pgvector, you run them locally with Docker at no cost. Budget ₹500-1,000 if you want to test with larger datasets on paid tiers.
How is this different from the RAG Masterclass?
This course goes deep into vector database internals, indexing, performance, and non-RAG use cases like recommendations. The RAG Masterclass covers the full RAG pipeline including generation, evaluation, and agentic patterns. This course is the storage layer; RAG Masterclass is the application layer. They complement each other.
Can I use open-source embedding models instead of OpenAI?
Yes. We cover open-source embedding models like BGE, E5, and MiniLM alongside OpenAI and Cohere. You can complete the entire course without paid API keys using open-source models and self-hosted databases.
Is this course updated when new vector databases or features are released?
Yes. We update within 30 days of major releases. The last update was July 2026, covering the latest Pinecone serverless, Qdrant 1.10+, Weaviate 1.25+, Milvus 2.4+, and pgvector 0.7+. Lifetime updates are included.
Will this help me choose a vector database for my company?
Absolutely. Module 9 includes a benchmarking framework, and the capstone project 4 is a multi-database benchmark. By the end, you will have hands-on experience with all five databases and a systematic framework for evaluating any new database.
Do I need a powerful computer for the self-hosted databases?
For learning purposes, 8GB RAM is sufficient to run Qdrant, Weaviate, Milvus, and pgvector locally with Docker. For the benchmarking project, 16GB is recommended. We provide cloud alternatives for students with limited hardware.
What to Learn Next
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