Author: Romina Sharifpour

Unlocking accurate and insightful answers from vast amounts of text is an exciting capability enabled by large language models (LLMs). When building LLM applications, it is often necessary to connect and query external data sources to provide relevant context to the model. One popular approach is using Retrieval Augmented Generation (RAG) to create Q&A systems […]

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Building a robust MLOps pipeline demands cross-functional […]