Session: Democratizing Deep Learning with Vector Similarity Search
Deep learning is responsible for most of the breakthroughs we have seen in AI/ML in recent years, yet most companies' models in production use classic or traditional ML. In this talk we will explore how deep learning is being democratized today, thanks to the rising use and availability of vector embeddings from giant pre-trained neural networks. We will see how these embeddings can be combined together with vector similarity search to address different use cases covering any modality and applied to any type of object. Finally, we will discuss the many opportunities this presents as well as the tools that are required to successfully deploy these applications into production.
- Why deep learning is revolutionizing the world
- The limitations of deep learning that make it inaccessible to most companies and impractical for many real world applications
- How deep learning is in the process of being democratized thanks to the rising use and availability of vector embeddings from giant pre-trained neural networks
- How by combining these embeddings with vector similarity search you can infuse your applications with AI, and apply them to real world use cases, without needing tons of money, expertise, compute power or huge datasets
- How vector databases allow you to scale your use cases to millions and even billions of vector embeddings with a query time of 200ms or less
Nava is a Developer Advocate for Data Science and MLOps. She started her career in tech with an R&D Unit in the IDF and later had the good fortune to work with and champion Cloud, Big Data, and DL/ML/AI technologies just as the wave of each of these was starting. Nava is also a mentor at the MassChallenge accelerator and the founder of LerGO—a cloud-based EdTech venture. In her free time she enjoys cycling, 4-ball juggling, and reading fantasy and sci-fi books.