Foundation Models perform well at natural language tasks such summarization, text generation and question answering on a broad variety of topics but struggle to provide accurate answers and without hallucinations about content that they haven't seen in their training data. Retrieval Augmented Generation (RAG) is a popular approach to mitigate this problem. When building next generation Information retrieval (IR) applications using a RAG architecture, enterprises encounter challenges of scale and operational complexity. In this talk, we will address these challenges and walkthrough a set open source technologies on AWS for building enterprise vector-based IR applications.
Vedant Jain is a Sr. AI/ML Specialist Solutions Architect, helping customers derive value out of the Machine Learning ecosystem at AWS. Prior to joining AWS, Vedant has held ML/Data Science Specialty positions at various companies such as Databricks, Hortonworks (now Cloudera) & JP Morgan Chase. Outside of his work, Vedant is passionate about making music, using Science to lead a meaningful life & exploring delicious cuisines from around the world.
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