DataHour: Significance of Vector Databases in Gen AI

DataHour: Significance of Vector Databases in Gen AI

30 Jan 202412:01pm - 30 Jan 202413:01pm

DataHour: Significance of Vector Databases in Gen AI

About the Event

Vector databases are designed to efficiently handle high-dimensional data, which is common in AI applications dealing with complex and diverse data types. Vector databases excel in similarity search, allowing for the retrieval of data points that are most similar to a given query vector. This is crucial in various AI tasks, including recommendation systems and image recognition.

Vector databases support the storage and retrieval of vector embeddings generated by machine learning models. This is essential for applications like natural language processing, where words and sentences are often represented as vectors. With the ability to efficiently index and search high-dimensional vectors, vector databases contribute to real-time analytics, enabling quick insights and decision-making in AI-driven applications.

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Who is this DataHour for?

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About the Speaker

Shanthababu Pandian

Shanthababu Pandian

Solution Architect (Open AI and Data) at JPOWER4

Shanthababu has 21+ years of experience in Information Technology (IT) Expertise in Data–Driven Analytics products, Cloud Data platform implementations, liaising with clients, gathering, eliciting requirements, architecting, and devising cost-effective solutions as per delivery frameworks and mitigating project delivery risks. He also has an expertise in providing insights involving requirement gathering, developing visualizing, and models using data for making the right business decisions through quantitative research, advanced data sourcing and data profiling for MSBI, Power BI, D356 and Azure cloud platforms.

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