This blog explores DeepSeek's hybrid training methodology, combining Supervised Learning and Reinforcement Learning, and emphasizes the critical role of real-time data orchestration for efficient LLM training. By showcasing how Meroxa’s platform enables dynamic data ingestion, seamless feedback loops, and scalable feature engineering, the blog provides actionable insights for professionals designing high-performance, real-time AI systems.
In the rapidly evolving world of AI, businesses are discovering that the future lies not in massive, general-purpose language models (LLMs) but in tiny, specialized models powered by real-time data streams. These domain-specific models offer dramatic cost savings, enhanced accuracy, and reduced hallucinations by continuously learning from live business data. From customer support to financial services and supply chain management, tiny models excel in delivering precise, actionable insights tailored to specific operations. Powered by platforms like Meroxa, which enables robust real-time data infrastructure, this approach bridges the gap between AI capabilities and business needs, providing a sustainable, efficient path to enterprise AI innovation.
