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2024 is the year of multimodal, and multimodal embeddings are some of the most exciting developments in this space. In this webinar, John Dickerson, Arthur's co-founder and Chief Scientist and Zach Nussbaum, Principal Machine Learning Engineer at Nomic discussed key concepts at the intersection of embeddings and ML observability, best practices for implementation, a behind-the-scenes look at building and training a multimodal embedding model, and use cases with multimodal RAG. They covered: - Multimodal embeddings and their applications - Current capabilities for monitoring embeddings, from NLP to computer vision - The latest research in the multimodal embedding space - What the future could look like for multimodal embeddings and, more generally, open-source AI innovation —— About Arthur: Arthur is the AI performance company. Our platform monitors, measures, and improves machine learning models to deliver better results. We help data scientists, product owners, and business leaders accelerate model operations and optimize for accuracy, explainability, and fairness. Arthur’s research-led approach to product development drives exclusive capabilities in LLMs, computer vision, NLP, bias mitigation, and other critical areas. We’re on a mission to make AI work for everyone, and we are deeply passionate about building ML technology to drive responsible business results. Learn more about Arthur → http://bit.ly/3KA31Vh Follow us on X → https://x.com/itsarthurai Follow us on LinkedIn → / arthurai Sign up for our newsletter → https://www.arthur.ai/newsletter