👋SIA: Custom & private language models within your business
LLMOps platform for deploying custom & private LLM-powered apps within your business ecosystem
Last updated
LLMOps platform for deploying custom & private LLM-powered apps within your business ecosystem
Last updated
With the release of OpenAI's ChatGPT, the floodgates have opened, ushering in a new era of large language models (LLMs) in production.
Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more. With access to massive amounts of data, LLMs have the potential to revolutionize the way we interact with language. Although LLMs are capable of performing various NLP tasks, they are considered generalists and not specialists. In order to train an LLM to become an expert in a particular domain, contextualization or fine-tuning, or both, are usually required.
One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. The sheer scale of these models requires high-performance computing resources, such as specialized GPUs with large amounts of memory. Additionally, the size of these models can make them computationally expensive, which can significantly increase training and inference times.
Suddenly, conversations about artificial intelligence infiltrate everyday life, as your neighbor seeks to engage in small talk about this groundbreaking technology. Moreover, the machine learning (ML) community has birthed a fresh buzzword: "LLMOps."
The advent of LLMs is revolutionizing the construction and maintenance of AI-powered products, necessitating the development of novel tools and best practices to navigate the lifecycle of LLM-powered applications.
