ZenML launches Pipeline Deployments, a new feature that transforms any ML pipeline or AI agent into a persistent, high-performance HTTP service with no cold starts and full observability.
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.
On custom evaluation frameworks for clinical RAG systems, showing why domain-specific metrics matter more than plug-and-play solutions when trust and safety are non-negotiable.
We're expanding ZenML beyond its original MLOps focus into the LLMOps space, recognizing the same fragmentation patterns that once plagued traditional machine learning operations. We're developing three core capabilities: native LLM components that provide unified APIs and management across providers like OpenAI and Anthropic, along with standardized prompt versioning and evaluation tools; applying established MLOps principles to agent development to bring systematic versioning, evaluation, and observability to what's currently a "build it and pray" approach; and enhancing orchestration to support both LLM framework integration and direct LLM calls within workflows. Central to our philosophy is the principle of starting simple before going autonomous, emphasizing controlled workflows over fully autonomous agents for enterprise production environments, and we're actively seeking community input through a survey to guide our development priorities, recognizing that today's infrastructure decisions will determine which organizations can successfully scale AI deployment versus remaining stuck in pilot phases.
FloraCast is a production-ready template that shows how to build a forecasting platform—config-driven experiments, model versioning/staging, batch inference, and scheduled retrains—with ZenML and Darts.
Comprehensive analysis of why simple AI agent prototypes fail in production deployment, revealing the hidden complexities teams face when scaling from demos to enterprise-ready systems.
Lessons from the Maven Evals course are combined with 50+ real-world case studies from ZenML's LLMOps Database to show how companies like Discord, GitHub, and Coursera implement the Three Gulfs model and Analyze-Measure-Improve lifecycle to transform failing LLM systems into production-ready applications.
287 latest curated summaries of LLMOps use cases in industry, from tech to healthcare to finance and more. This blog also highlights some of the trends observed across the case studies.
ZenML's new DXT-packaged MCP server transforms MLOps workflows by enabling natural language conversations with ML pipelines, experiments, and infrastructure, reducing setup time from 15 minutes to 30 seconds and eliminating the need to hunt across multiple dashboards for answers.
We're expanding ZenML beyond its original MLOps focus into the LLMOps space, recognizing the same fragmentation patterns that once plagued traditional machine learning operations. We're developing three core capabilities: native LLM components that provide unified APIs and management across providers like OpenAI and Anthropic, along with standardized prompt versioning and evaluation tools; applying established MLOps principles to agent development to bring systematic versioning, evaluation, and observability to what's currently a "build it and pray" approach; and enhancing orchestration to support both LLM framework integration and direct LLM calls within workflows. Central to our philosophy is the principle of starting simple before going autonomous, emphasizing controlled workflows over fully autonomous agents for enterprise production environments, and we're actively seeking community input through a survey to guide our development priorities, recognizing that today's infrastructure decisions will determine which organizations can successfully scale AI deployment versus remaining stuck in pilot phases.
