LLM observability: Your guide to monitoring AI in production
Deploying LLM applications into production is complex. This guide explains LLM observability - why it matters, common failure modes like hallucinations, key tool features, and how to get started with W&B Weave.
AI agents in healthcare: Enhancing patient outcomes and streamlining operations
This article explores how AI agents are revolutionizing healthcare by enhancing clinical decision-making, personalizing patient care, automating administrative tasks, and addressing key challenges to drive safer, more efficient, and patient-centered outcomes.
Generative AI is revolutionizing the financial services industries by automating complex tasks, enhancing customer interactions, and bolstering security. In banking, generative AI models can generate…
Modern quant research at scale: GPU-native compute, unified scheduling, and W&B experiment tracking to turn exploration into deployable trading systems.
On this page Executive summary The transfornative potential Understanding agents in banking Technical architecture Use cases Production readiness checklist Challenges and solutions Ethical and responsible…
Reinforcement learning: A guide to AI’s interactive learning paradigm
On this page What is reinforcement learning? The goal Online vs offline RL Taxonomy Core methods Benchmarks, metrics, and frameworks Advances and trends Successful applications…
The rise of large language models (LLMs) has revolutionized natural language processing, opening the door to powerful applications across industries—from conversational agents and code generation…
What are AI agents? Key concepts, benefits, and risks
In this exploration, we will dive into the architecture, applications, and future potential of AI agents, highlighting their role in transforming modern problem-solving paradigms.
Generative AI is reshaping the retail industry, ushering in a new era of personalization, operational efficiency, and innovation. As this technology advances, retailers are leveraging…
Current best practices for training LLMs from scratch
Explore the foundational considerations for training large language models (LLMs) from scratch, including key trade-offs, pitfalls, and decision-making frameworks.
