Vector Databases for AI & ML

Vector Databases for AI & ML | Slides

Vector databases are a type of database designed to handle high-dimensional data, particularly in the field of AI and machine learning. They allow for efficient storage, processing, and computation on data represented as vectors. These databases are crucial in performing operations such as similarity search and nearest neighbor search on high-dimensional data, providing faster and more efficient processing.


VECTOR DB SLIDES VECTOR DB SLIDES	WHAT IS VECTOR DB WHAT IS VECTOR DB	USES OF VECTOR DB USES OF VECTOR DB
VECTOR DB VS TRADITIONAL DB SL VECTOR DB VS TRADITIONAL DB SL	VECTOR DB VENDORS VECTOR DB VENDORS	VECTOR DB FEATURES VECTOR DB FEATURES
WHAT ARE VECTORS WHAT ARE VECTORS	CRITERIA TO SELECT VECTOR DB CRITERIA TO SELECT VECTOR DB	VECTOR DB VS ELASTIC SEARCH VECTOR DB VS ELASTIC SEARCH
VECTOR DB DIMENSIONS VECTOR DB DIMENSIONS	VECTOR DB CRUD VECTOR DB CRUD	CHALLENGES FREQUENT UPDATE CHALLENGES FREQUENT UPDATE
VECTOR DB APPLICATIONS VECTOR DB APPLICATIONS

Vector Databases	Importance	Usage
Vector databases are a type of database that store, process, and perform computations on data in the form of vectors. They are designed to handle high-dimensional data and are particularly useful in the field of machine learning and artificial intelligence, where data is often represented as high-dimensional vectors. Vector databases can perform operations such as similarity search and nearest neighbor search efficiently.	With the rise of AI and machine learning, the need for efficient handling and processing of high-dimensional data has increased. Traditional databases are not designed to handle this type of data efficiently, leading to the emergence of vector databases. They allow for faster and more efficient processing of high-dimensional data, making them crucial in AI and machine learning applications.	Vector databases are used when dealing with high-dimensional data, particularly in the field of AI and machine learning. They are used when operations such as similarity search and nearest neighbor search need to be performed on the data. They are also used when the data needs to be processed and analyzed quickly and efficiently.

Here we discuss the concept of vectors, their dimensions, significance in various fields, and data transformation operations, highlighting their crucial role in representing and manipulating data efficiently across disciplines like data science and machine learning.

Vector databases are designed for handling vector data and are optimized for data parallelism, making them ideal for large datasets that require high-performance computing. They are commonly used in fields such as data analytics, machine learning, and scientific computing. On the other hand, NoSQL databases are non-relational databases that can handle large volumes of unstructured data and are known for their scalability and flexible schemas. They are best suited for situations where data doesn't fit neatly into a table or when the data structure may change over time. NoSQL databases are often used in big data and real-time web applications.

Vector databases and NoSQL both handle unstructred data. Vector DB store embeddings and used in machine learning e.g. AI Assistants, recommendations, search etc.. NoSQL store real data and used for querying e.g. Big data scenarios nd also used in real time web applications.

In this slide we discuss the efficient storage and management of vector data using specialized vector databases, highlighting their role in similarity search tasks through algorithms like nearest neighbor search and various indexing techniques.

Vector databases store data in a vector format, enabling efficient storage and retrieval based on similarity metrics. In retrieval augmented generation, these databases enhance search and recommendation systems by quickly retrieving similar items using vector representations, facilitating personalized recommendations and efficient search functionalities.

Learn about the essential CRUD operations (Create, Read, Update, Delete) on vector data, which is crucial for effectively managing and manipulating spatial information. The guide covers creating new data, reading attributes and geometry, updating existing features, and deleting outdated or erroneous data from the database.

Here are real-world applications of geospatial technology across various industries, including logistics, retail, urban planning, healthcare, and agriculture. It highlights how location-based services are utilized for route optimization, marketing strategies, city infrastructure management, disease tracking, and precision farming to enhance efficiency and decision-making processes.

The cost of a vector database is influenced by factors such as the number of indices, dimensions of the data, inference/access frequency, storage space, and processing power required.

Here is a list of interview questions for evaluating candidates' knowledge and understanding of vector databases, including their definition, usage, advantages and disadvantages, indexing, and data security measures.

Challenges-frequent-update Criteria-to-select-vector-db Crud Operations For Vector DB Uses-of-vector-db Vector-db-applications Vector-db-crud Vector-db-dimensions Vector-db-features Vector-db-impact-invarious-fi Vector-db-rag

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