How Vector Similarity Search and Generative AI can help you build Smart Apps
In this talk, we'll learn how to leverage machine learning models and cutting-edge software tools —including Vector Similarity Search, Embeddings, Generative AI, Prompt Engineering and Retrieval Augmentation—to build smart applications such as recommendation engines, semantic search, and chatbots with intelligent human-like conversations. While deep learning models like GPT, BERT, CLIP, and YOLO have revolutionized AI and machine learning, most companies still don't utilize them for real-world applications due to their resource requirements: huge datasets, computing power, and data science expertise. Generative AI and Vector Databases provide a more practical alternative for infusing AI into your applications by leveraging vector embeddings from giant pre-trained models (e.g., Large Language Models, Large Vision Models) and vector similarity search techniques. This session will explain the key concepts behind using vector embeddings for similarity search, selecting the right pre-trained deep learning model, generating vector embeddings, and how to leverage the latest innovations in generative AI in conjunction with vector databases. We'll also provide real-world examples demonstrating how these techniques can be applied in practice. Join us to learn how you can leverage embeddings, vector databases for vector similarity search, and generative AI to affordably bring state-of-the-art AI power to your projects.
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