Greetings, computer vision practitioners👮! I hope you're all doing well in your projects. Recently, I received a new requirement from a client regarding an object detection solution that had been successfully deployed as a REST API. The solution was seamlessly integrated into both web and mobile applications. However, the client encountered network reliability issues, particularly in rural areas where poor connectivity occasionally caused the solution to fail on mobile devices💔. As we know, network disruptions are a common challenge, but they become critical when they impact business operations. My client sought a robust solution to address this issue, ensuring uninterrupted functionality regardless of network conditions. In this blog post, we will explore how I tackled this challenge and implemented an effective solution💓. The Solution: Enabling Offline Object Detection for Mobile Devices Given that network connectivity issues are beyond our control, the most effective soluti...
Another blog post starts with you beautiful people👦. I hope you have explored my last blog post about 2x faster fine-tuning of Mistral 7b model on a custom dataset👈. In this blog post, we are going to learn an essential technique in Generative AI: Retrieval Augmented Generation (RAG). What is RAG? Retrieval Augmented Generation (RAG) is an innovative approach that melds generative models, like transformers, with a retrieval mechanism. By tapping into existing knowledge, RAG retrieves pertinent information from expansive external datasets or knowledge bases to enhance the generation process, thereby elevating the model's content relevance and factual accuracy💪. This versatility renders RAG particularly beneficial for tasks demanding the assimilation of external knowledge, such as question answering or content creation. Upon receiving input, RAG actively searches for relevant documents from specified sources (e.g., Wikipedia, company knowledge base, etc.). It th...