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Ahmedabad combines regional culture, business activity and growing demand for professional skills.
Ahmedabad, India
Generative AI
Vector Databases (Pinecone, Weaviate, Milvus)in Ahmedabad
Vector Databases training in Ahmedabad, India 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…
40 Hours/5 daysLive virtual trainingIntermediate
Classroom training and venue arrangements depend on the availability of a suitable conference hall in Ahmedabad, India. Dates, seating capacity, facilities and the final venue are confirmed before booking.
Learning in Ahmedabad, India
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Classroom availability and venue
Classroom training and venue arrangements depend on the availability of a suitable conference hall in Ahmedabad, India. Dates, seating capacity, facilities and the final venue are confirmed before booking.
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Explore Vector Databases (Pinecone, Weaviate, Milvus)
Vector Databases training in Ahmedabad, India 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.
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Learning from Ahmedabad
Ahmedabad combines regional culture, business activity and growing demand for professional skills.
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City-page course benefits
- Master Vector Database Technologies – Gain practical knowledge of Pinecone, Weaviate, and Milvus for building modern AI and search applications.
- Learn Vector Embeddings – Understand how text and other data are transformed into vector representations for semantic search and AI applications.
- Develop Semantic Search Skills – Build intelligent search solutions that identify relevant information based on meaning rather than simple keyword matching.
- Understand Vector Indexing – Learn indexing techniques that enable efficient similarity search across large collections of vector data.
- Build RAG Applications – Apply vector databases to Retrieval-Augmented Generation workflows for knowledge-based LLM applications in Ahmedabad, India.
- Work with Pinecone – Learn practical techniques for managing vector data, namespaces, metadata, indexing, and similarity search using Pinecone.
- Explore Weaviate – Develop hands-on understanding of Weaviate collections, vector search, filtering, and AI-powered data retrieval.
- Learn Milvus Development – Work with Milvus concepts for storing, indexing, and searching high-dimensional vector data at scale.
- Implement Metadata Filtering – Learn how metadata can be combined with vector similarity to improve search accuracy and application results.
- Build AI-Powered Search Solutions – Develop semantic search, recommendation, knowledge discovery, and intelligent retrieval applications using vector databases.
- Improve LLM Application Development – Understand how vector databases provide relevant context to LLMs and enhance AI application capabilities.
- Gain Industry-Ready Vector Database Skills – Develop practical expertise applicable to AI engineering, RAG development, semantic search, and Generative AI projects in Ahmedabad, India.
Course curriculum
- 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 Ahmedabad, India
- 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
Exam details
- 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 Ahmedabad, India
Plan your learning
Who this course is for
AI and Machine Learning Engineers – Professionals who want to develop practical expertise in vector databases, embeddings, similarity search, and AI retrieval systems. Software Developers – Developers looking to integrate Pinecone, Weaviate, and Milvus into Python applications, APIs, semantic search systems, and AI solutions. Generative AI Professionals – AI practitioners seeking to build RAG applications, LLM-powered solutions, knowledge bases, and intelligent retrieval workflows. RAG Developers and Engineers – Professionals interested in designing document ingestion, embedding, vector storage, retrieval, and context-generation pipelines. Data Engineers – Data professionals who want to work…
Before you begin
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…
Learning 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…