ZenML's new Quick Wins skill for Claude Code automatically audits your ML pipelines and implements 15 best-practice improvements (from metadata logging to Model Control Plane setup) based on what's actually missing in your codebase.
Analysis of 1,200 production LLM deployments reveals six key patterns separating successful teams from those stuck in demo mode: context engineering over prompt engineering, infrastructure-based guardrails, rigorous evaluation practices, and the recognition that software engineering fundamentals—not frontier models—remain the primary predictor of success.
ZenML's Pipeline Deployments transform pipelines into persistent HTTP services with warm state, instant rollbacks, and full observability—unifying real-time AI agents and classical ML models under one production-ready abstraction.
How to build a production-ready financial report analysis pipeline using multiple specialized AI agents with ZenML for orchestration, SmolAgents for lightweight agent implementation, and LangFuse for observability and debugging.
In this CrewAI pricing guide, we discuss the costs, features, and value CrewAI provides to help you decide if it’s the right investment for your business.
