Weekend batches
October 2026
09:00–15:00 · flexible hours
$850per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI
Vector Databases (Pinecone, Weaviate, Milvus) in North Korea
Vector Databases training in North Korea provides practical skills to work with Pinecone, Weaviate, and Milvus for modern AI applications. Learn vector embeddings, similarity search, indexing, metadata filtering, hybrid search, and Retrieval-Augmented Generation (RAG). This course helps developers and AI professionals build scalable semantic search systems, knowledge bases, recommendation solutions, and LLM-powered applications using leading vector database technologies.
40 Hours/5 daysIntermediate4.3 · 1,000 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 40 Hours/5 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$850per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$850per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$850per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
40 Hours/5 daysIntermediate
- Module 1: Introduction to Vector Databases
- Understanding vector databases and their role in modern AI applications
- Vector databases vs. traditional relational and NoSQL databases
- Use cases including semantic search, recommendations, knowledge bases, and Generative AI
- Overview of Pinecone, Weaviate, and Milvus
- Module 2: Vector Embeddings Fundamentals
- Understanding vectors, embeddings, dimensions, and vector representations
- Text, image, audio, and multimodal embeddings
- Embedding models and similarity representation
- Choosing appropriate embedding models for AI applications
- Module 3: Similarity Search and Distance Metrics
- Understanding nearest-neighbor and approximate nearest-neighbor search
- Cosine similarity, Euclidean distance, and dot-product similarity
- Query vectors and similarity scoring
- Implementing basic semantic search workflows
- Module 4: Vector Indexing Techniques
- Understanding vector indexes and their role in efficient retrieval
- Exploring HNSW, flat indexing, and approximate nearest-neighbor techniques
- Index configuration and search-performance considerations
- Balancing search accuracy, latency, memory, and scalability
- Module 5: Pinecone Fundamentals
- Understanding Pinecone architecture and core concepts
- Creating indexes and managing vector collections
- Inserting, updating, deleting, and retrieving vectors
- Performing similarity searches with metadata
- Working with Pinecone APIs and SDKs
- Module 6: Weaviate Fundamentals
- Understanding Weaviate architecture, collections, objects, and vectors
- Creating collections and managing vectorized data
- Performing vector and semantic searches
- Working with Weaviate APIs and client libraries
- Exploring filtering and retrieval capabilities
- Module 7: Milvus Fundamentals
- Understanding Milvus architecture and core components
- Creating collections, schemas, fields, and indexes
- Loading and managing vector data
- Performing similarity searches and filtered queries
- Working with Milvus SDKs and APIs
- Module 8: Data Ingestion and Vector Management
- Preparing structured and unstructured data for vector storage
- Generating embeddings and inserting vectors
- Managing vector IDs, metadata, and document attributes
- Updating and deleting vector records
- Designing efficient ingestion pipelines
- Module 9: Metadata and Attribute Filtering
- Understanding metadata associated with vector records
- Applying filters to narrow search results
- Combining vector similarity with structured conditions
- Designing metadata schemas for efficient retrieval
- Implementing filtered vector search workflows
- Module 10: Semantic and Hybrid Search
- Understanding semantic search and keyword-based search
- Combining vector similarity with keyword retrieval
- Configuring hybrid search strategies
- Comparing search relevance and retrieval performance
- Building practical hybrid search applications
- Module 11: Vector Databases for RAG
- Understanding the role of vector databases in Retrieval-Augmented Generation
- Document ingestion, chunking, embedding, and indexing
- Retrieving relevant context for LLM prompts
- Connecting vector databases with RAG pipelines
- Building a basic vector-based question-answering application
- Module 12: Vector Database Integration with Python
- Connecting Python applications with Pinecone, Weaviate, and Milvus
- Using SDKs, REST APIs, and authentication
- Creating reusable vector search functions
- Handling queries, responses, errors, and API operations
- Building Python-based semantic search applications
- Module 13: Advanced Retrieval Techniques
- Understanding top-k retrieval and similarity thresholds
- Applying metadata-based retrieval strategies
- Exploring reranking and result refinement
- Improving retrieval relevance and precision
- Designing multi-stage retrieval workflows
- Module 14: Performance Optimization
- Optimizing vector indexes and search parameters
- Managing latency, memory usage, and retrieval accuracy
- Understanding indexing and compression considerations
- Evaluating query performance across different workloads
- Applying optimization strategies for large-scale vector collections
- Module 15: Scalability and Production Architecture
- Designing scalable vector database architectures
- Understanding distributed vector search concepts
- Managing large datasets and high-volume queries
- Exploring availability, monitoring, and operational considerations
- Selecting suitable deployment approaches for production AI applications
- Module 16: Security and Data Management
- Understanding authentication, authorization, and API key management
- Managing sensitive data and access controls
- Applying secure data ingestion and retrieval practices
- Understanding backup, recovery, and data lifecycle considerations
- Designing secure vector database integrations
- Module 17: Practical AI Search Project
- Designing a complete semantic search application in North Korea
- Preparing documents and generating vector embeddings
- Storing and retrieving vectors using a selected vector database
- Implementing metadata filtering and similarity search
- Integrating retrieval with an LLM-powered application
- Testing and evaluating the final solution
- Module 18: Capstone Project and Best Practices
- Comparing Pinecone, Weaviate, and Milvus for different AI use cases
- Designing a production-ready vector database workflow
- Evaluating retrieval quality, performance, and scalability
- Troubleshooting common implementation challenges
- Applying best practices for maintainable vector database solutions
Entry requirements and prerequisites
- Python Programming Knowledge – Basic understanding of Python, functions, classes, packages, and API calls is recommended for practical vector database development.
- Database Fundamentals – Familiarity with databases, collections, records, indexing, querying, and basic data management concepts will help learners understand vector databases.
- Generative AI Fundamentals – Basic knowledge of Generative AI, Large Language Models (LLMs), and AI-powered applications is beneficial.
- Embeddings Concepts – Understanding the basic idea of text embeddings, vectors, and semantic representations is helpful for learning vector search.
- REST API Knowledge – Familiarity with REST APIs, JSON, authentication, and API keys is useful when working with Pinecone, Weaviate, and Milvus.
- RAG Fundamentals – Basic awareness of Retrieval-Augmented Generation and semantic search will help learners understand practical vector database use cases.
- Development Environment Familiarity – Experience with Python environments, package installation, command-line tools, and Git is recommended for hands-on exercises in North Korea.
- Docker Knowledge – Basic Docker familiarity is helpful for learners who want to work with locally hosted vector database environments such as Weaviate or Milvus.
Career value
Reasons to choose this course
Learn Leading Vector Database Technologies
Gain practical knowledge of Pinecone, Weaviate, and Milvus, three widely used technologies for modern vector search and AI applications.
Master Vector Embeddings
Understand how embeddings represent text and other data as vectors for efficient semantic retrieval.
Build Semantic Search Solutions
Learn to develop intelligent search systems that retrieve information based on meaning rather than exact keyword matches.
Develop RAG Applications
Build Retrieval-Augmented Generation solutions by connecting vector databases with LLM-powered applications.
Understand Similarity Search
Learn similarity metrics, nearest-neighbor search, and retrieval techniques used in AI applications.
Work with Vector Indexing
Explore indexing approaches that help improve search performance across large vector collections.
Advantages
Turn learning into practical capability
Choose the Right Vector Database
Develop the ability to evaluate Pinecone, Weaviate, and Milvus based on application requirements, infrastructure preferences, scalability, and operational needs.
Improve AI Application Architecture
Learn how vector databases can become a dedicated retrieval layer within RAG, recommendation, search, and other AI application architectures.
Gain Deployment Flexibility
Understand managed-cloud and self-hosted approaches, helping teams select deployment models that align with their technical and business requirements.
Enhance Development Productivity
Build familiarity with database APIs, SDKs, indexing approaches, and integration patterns that can streamline the development of vector-powered applications.
Prepare for Production AI Workloads
Understand architectural considerations such as scalability, availability, indexing, data management, and operational requirements when moving AI applications toward production.
Expand Career Opportunities in AI Engineering
Build specialized vector database expertise that can complement skills in Python, Machine Learning, Generative AI, RAG, LLMs, and cloud technologies for AI-focused roles.
Exam format & assessment
- Exam Level: Intermediate – Vector Databases
- Exam Type: Online computer-based assessment
- Number of Questions: 40 questions
- Duration: 90 minutes
- Passing Score: 70% (28 out of 40)
- Question Format: Multiple-choice and practical/scenario-based questions
- Exam Topics: Vector embeddings, similarity search, indexing, Pinecone, Weaviate, Milvus, metadata filtering, hybrid search, RAG, performance, and scalability
- Certification: StepMerit Learning Course Completion Certificate upon successful completion in North Korea
Bring your learning together
Course conclusion
The Vector Databases (Pinecone, Weaviate, Milvus) course provides practical skills to build scalable semantic search, RAG, recommendation, and AI-powered applications. Through hands-on learning, participants gain experience with vector embeddings, similarity search, indexing, metadata filtering, hybrid search, Python integration, and production considerations. This training helps developers and AI professionals strengthen their expertise in modern AI data infrastructure and prepare for industry-relevant opportunities in North Korea.
Target audience
- AI and Machine Learning Engineers
- Software Developers
- Generative AI Professionals
- RAG Developers and Engineers
- Data Engineers
- AI Application Developers
- Backend and API Developers
- Cloud and AI Infrastructure Professionals
- Search and Recommendation Developers
- LLM Application Developers
- AI Solutions Architects and Consultants
- AI Enthusiasts and Career Switchers
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific expertise
- Trainer Experience: 5+ years of professional and training experience in AI, ML, Generative AI, and vector database technologies
- Course Training Experience: Delivered 100+ hours of training specifically covering vector databases, embeddings, semantic search, and RAG implementations
- Candidates Trained: 150+ candidates across different technical and professional backgrounds
- Training Delivery: Instructor-led online and classroom-based sessions with demonstrations, hands-on labs, exercises, and real-world use cases
- Technologies Covered: Pinecone, Weaviate, Milvus, vector embeddings, similarity search, metadata filtering, indexing, and RAG integration
- Practical Exposure: Guided learners through end-to-end implementation of vector database solutions using real-world scenarios
- Industry Use Cases: Healthcare, finance, e-commerce, customer support, enterprise search, and knowledge management
- Hands-On Training: Learners work on document ingestion, chunking, embedding generation, vector storage, retrieval, filtering, and search evaluation
- Project Guidance: Supported learners in developing practical semantic-search and RAG-based projects using vector databases
- Assessment Experience: Evaluated learner performance through practical assignments, implementation exercises, troubleshooting activities, and project-based assessments
Documented learner outcomes
- Problem Statement: Healthcare organizations often store large volumes of medical documents, clinical guidelines, research papers, patient education materials, and treatment information in unstructured formats. Traditional keyword-based search may fail to understand semantic meaning, making it difficult for healthcare professionals to quickly retrieve relevant information.
- How We Overcame the Problem: Learners designed a semantic search solution using vector embeddings and vector databases. Healthcare documents were processed, converted into embeddings, and stored in Pinecone, Weaviate, and Milvus. Similarity search was then used to retrieve the most relevant information based on the meaning of a query rather than exact keywords.
- Overall Implementation: Learners implemented an end-to-end healthcare information retrieval workflow covering document ingestion, text preprocessing, chunking, embedding generation, vector storage, metadata management, similarity search, filtering, result ranking, and retrieval validation.
- Evidence of Learning: Learners demonstrated the ability to create collections/indexes, generate and store embeddings, perform semantic similarity searches, apply metadata filters, compare Pinecone, Weaviate, and Milvus, and evaluate retrieval quality.
- Healthcare Search Workflow:
Medical Documents → Data Cleaning → Text Chunking → Embedding Generation → Vector Database → Similarity Search → Relevant Medical Information → Search Results - Pinecone Implementation: Learners created a vector index for healthcare knowledge, inserted document embeddings with metadata such as document type and medical category, and performed similarity searches to retrieve relevant content.
- Weaviate Implementation: Learners created a semantic search collection, stored healthcare document vectors and metadata, and used vector-based queries and filtering to identify relevant medical information.
- Milvus Implementation: Learners created a vector collection, configured appropriate indexing and similarity-search parameters, loaded healthcare document embeddings, and retrieved the most relevant results.
- Problem-Solving Evidence: Learners addressed challenges such as irrelevant keyword matches, large document sizes, duplicate information, metadata filtering, embedding consistency, and retrieval accuracy by applying chunking strategies, semantic embeddings, metadata filters, and appropriate vector-search configurations.
- Comparative Evidence: Learners compared Pinecone, Weaviate, and Milvus based on indexing, search capabilities, metadata handling, scalability, deployment considerations, and suitability for healthcare-oriented semantic search applications.
- Overall Outcome: By completing the implementation, learners demonstrated practical skills in building a vector-search solution that can serve as the retrieval layer for healthcare AI applications such as medical knowledge assistants, clinical information search, healthcare document discovery, and Retrieval-Augmented Generation (RAG) systems.
- Final Evidence: A working healthcare semantic-search prototype, source documents, processed document chunks, generated embeddings, vector database collections/indexes, search queries, retrieved results, comparison findings, and implementation documentation provide evidence of the learner's practical competency.
Helpful answers for planning your training
Frequently asked questions
A vector database stores and retrieves high-dimensional vector embeddings for AI-powered search and retrieval applications.
They help LLM applications efficiently retrieve relevant information from large datasets.
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