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 new pipeline deployments feature lets you use the same pipeline syntax to run both batch ML training jobs and deploy real-time AI agents or inference APIs, with seamless local-to-cloud deployment via a unified deployer stack component.
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.
Discover why production teams are treating agentic workflows as MLOps evolution, not revolution—plus how ZenML achieved 200x performance improvements for enterprise ML operations. Real insights from 130+ MLOps engineers on building reliable AI systems.
ZenML 0.80.0 transforms tenant structures into workspace/project hierarchies with advanced RBAC for Pro users, while enhancing tagging, resource filtering, and dashboard design. Open-source improvements include Kubernetes security upgrades, SkyPilot integration, and significantly faster CLI operations. Both Pro and OSS users benefit from dramatic performance optimizations, GitLab improvements, and enhanced build tracking.
The OpenPipe integration in ZenML bridges the complexity of large language model fine-tuning, enabling enterprises to create tailored AI solutions with unprecedented ease and reproducibility.
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