Large multimodal models (LMMs) represent a significant advancement in artificial intelligence, enabling AI systems to process and comprehend multiple types of data modalities such as text, images, audio, and video.
Content-based recommendation systems leverage the intrinsic features of items (such as movies, songs, or books) to make personalized suggestions.
As we navigate the complexities of financial fraud, the role of machine learning emerges not just as a tool but as a transformative force, reshaping the landscape of fraud detection and prevention.
Small Language Models (SLMs) are increasingly gaining attention and adoption among enterprises for their unique advantages and capabilities.
Testing large language models in production helps ensure their robustness, reliability, and efficiency in serving real-world use cases, contributing to trustworthy and high-quality AI systems.
A domain-specific language model constitutes a specialized subset of large language models (LLMs), dedicated to producing highly accurate results within a particular domain.
Follow Us
