Vector databases are specialized systems designed to store, manage, and query high-dimensional vector data. They excel in handling complex data types like embeddings from machine learning models, enabling fast similarity search and analysis. Vector databases support scalable storage and retrieval, ensuring efficient performance for applications like recommendation systems and natural language processing.

• Basic knowledge of programming concepts and data structures will be beneficial. 
• Understanding fundamental database principles and familiarity with high-dimensional data and machine learning concepts is also helpful. 

Module 1: Introduction to Vector Databases

• What is a Vector Database?

  • Overview of vector-based data representation.
  • Use cases of vector databases in AI, machine learning, and data analytics.
  • Difference between traditional databases and vector databases.

• Applications of Vector Databases

  • Search and retrieval in recommendation systems.
  • Semantic search: Using vectors for text search.
  • Image recognition and feature extraction with vectors.
  • Natural Language Processing (NLP) and embeddings.

• Fundamentals of Vectors in AI

  • What are vectors and vector embeddings?
  • The role of vectors in machine learning and deep learning.
  • How vectors represent data in high-dimensional spaces.
  • Dimensionality reduction techniques (e.g., PCA, t-SNE).

Module 2: Architecture and Working of Vector Databases

• Vector Database Components

  • Data storage: How vectors are stored and indexed.
  • Query engine: Efficient querying and search mechanisms.
  • Indexing methods for vector data (e.g., Approximate Nearest Neighbor Search (ANN)).

• Vector Search Algorithms

  • Linear Search vs. Approximate Nearest Neighbor Search (ANN).
  • Popular vector search algorithms: KNN (K-Nearest Neighbors), HNSW (Hierarchical Navigable Small World), and PQ (Product Quantization).
  • Cosine similarity, Euclidean distance, and other distance metrics for vector comparison.

• Indexing Structures for Vector Databases

  • KD Trees, Ball Trees, and LSH (Locality-Sensitive Hashing).
  • Understanding how vector indexing enables fast search and retrieval.
  • Comparison of ANN libraries: Faiss, Annoy, and NMSLIB.

• Database Architecture

  • Horizontal scaling and sharding for vector databases.
  • Integration with traditional relational databases.
  • Handling large-scale data: Partitioning and replication.

Module 3: Key Vector Database Technologies

• Popular Vector Databases

  • FAISS (Facebook AI Similarity Search):

    • Overview of FAISS.
    • Installing and setting up FAISS.
    • Indexing, search, and clustering with FAISS.
    • Using FAISS for large-scale nearest neighbor search.

  • Milvus:

    • Introduction to Milvus as an open-source vector database.
    • Milvus architecture and key features.
    • Setting up Milvus and integrating with AI models.
    • Performing efficient vector search and retrieval.

  • Pinecone:

    • Overview of Pinecone’s managed vector database service.
    • Integrating Pinecone with machine learning pipelines.
    • Using Pinecone for fast, scalable, and real-time vector searches.

  • Weaviate:

    • Introduction to Weaviate and its vector search capabilities.
    • Weaviate’s integration with machine learning models and external data sources.
    • Setting up and configuring Weaviate for semantic search.

Module 4: Vector Data Storage and Management

• Vector Data Storage Formats

  • Understanding how vectors are stored: Dense vs Sparse vectors.
  • Storing vectors in formats like JSON, Parquet, or binary formats.
  • Optimizing data storage and retrieval for high-dimensional data.

• Managing Large-Scale Vector Data

  • Techniques for scaling vector databases.
  • Storing and indexing large datasets (millions of vectors).
  • Batch insertion and real-time data insertion in vector databases.

• Optimizing Storage and Search Performance

  • Vector compression and quantization techniques.
  • Using sharding and partitioning for distributed vector data.
  • Load balancing and fault tolerance for high availability.

Module 5: Querying and Searching in Vector Databases

• Querying Vector Databases
  • Writing queries to retrieve similar vectors.
  • Using vector distance metrics (cosine similarity, Euclidean distance) for searching.
  • Filtering results by additional attributes.
• Building Search Systems
  • Setting up a search pipeline for vector queries.
  • Real-time vector searches and fast query response times.
  • Handling vector search latency and high-throughput systems.
• Ranking and Relevance
  • Ranking vectors by similarity score.
  • Custom relevance models based on query types.
  • Handling different types of queries in vector databases (e.g., nearest neighbors, range queries).

Module 6: Integrating Vector Databases with Machine Learning

• Vector Embeddings

  • Introduction to embeddings in machine learning.
  • Generating embeddings from text (Word2Vec, GloVe, BERT).
  • Generating embeddings from images (CNNs, ResNet, VGG).
  • Using pre-trained embeddings and fine-tuning models.

• Building a Search System with Machine Learning

  • Creating a recommendation system using vector embeddings.
  • Using vector embeddings for content-based filtering.
  • Building a semantic search engine with pre-trained models and vector databases.

• Data Pipelines and Vector Storage

  • Integrating vector storage with machine learning pipelines.
  • Batch processing and real-time updates in vector databases.
  • Using vector databases as a backend for AI-driven applications (e.g., search engines, personalization systems).

Module 7: Advanced Features and Techniques

• Vector Database Performance Tuning

  • Optimizing query performance for large vector datasets.
  • Fine-tuning index settings and configuration parameters.
  • Evaluating search quality: Precision, Recall, and F1 Score.

• Handling Multi-modal Data

  • Storing and querying multi-modal data (text, images, audio) in vector databases.
  • Combining vectors from different modalities for hybrid search.
  • Multi-modal embedding techniques and applications.

• Vector Data Versioning and Updates

  • Managing and updating vector data over time.
  • Handling versioning and incremental updates in vector databases.
  • Using vector databases for continuous learning and model updates.

Module 8: Real-World Use Cases and Projects

• Building a Semantic Search Engine

  • Overview of semantic search and its applications.
  • Implementing semantic search using vector databases and embeddings.
  • Case study: Building a semantic search engine with FAISS or Milvus.

• Recommendation Systems

  • Overview of recommendation algorithms (collaborative, content-based).
  • Building a vector-based recommendation system using Pinecone or Weaviate.
  • Case study: Building a movie recommendation system.

• AI and Machine Learning Applications

  • Using vector databases in AI-powered systems: Personal assistants, image search, and voice search.
  • Case study: Implementing AI-powered image search using vector databases.
  • Real-time recommendation engines and scalability challenges.

40 Days (also available fast track course with short term duration)

• Flexible Schedules
• Live Online Training

• Training by highly experienced and certified professionals
• No slideshow (PPT) training, fully Hand-on training
• Interactive session with interview QA’s
• Real-time projects scenarios & Certification Help
• 24 X 7 Support