Small language models (SLMs) enable faster, efficient, and on-device AI, reducing costs while making advanced AI accessible to more users and businesses.
Update edge AI models efficiently using Mix Up and contribution sampling to overcome domain shift with minimal data, ensuring continuous evolution without forgetting.
Agentic AI can transform testing—but only if it’s controlled. Start small, add guardrails, integrate tools, and scale autonomy once reliability and cost are proven.
Legacy systems are full of free-text fields where valuable business data goes to die. NLP pipelines turn messy maintenance logs into structured, actionable insights.
This study examines raw agent systems, from single-agent frameworks to multi-agent networks, and discusses LangGraph implementations and their significant challenges.
While large language models (LLMs) dominate the AI conversation, AutoML remains the king for structured data. Here’s how to choose the right tool for your infrastructure.
Token costs are bottlenecking AI systems. Learn how TOON, a token-oriented format, cuts LLM costs and boosts efficiency at scale for high-volume pipelines.
LLMs reshape data engineering by automating ETL tasks, enabling natural language analytics, and empowering faster, smarter decision-making without replacing engineers.
This post discusses codifying system constraints as executable code to detect and prevent architectural drift in AI deployments across CI, runtime, and operations.
AI now uses diverse data types, and old pipelines struggle. Unified data flows centralize data, simplifying management and improving model training and performance.
CORE
