Learn how to build production-ready agentic AI workflows that combine powerful research capabilities with enterprise-grade observability, reproducibility, and cost control using ZenML's structured approach to controlled autonomy.
Learn when to upgrade from open-source ZenML to Pro features with our subway-map guide to scaling ML operations for growing teams, from solo experiments to enterprise collaboration.
Discover the new ZenML MCP Server that brings conversational AI to ML pipelines. Learn how this implementation of the Model Context Protocol allows natural language interaction with your infrastructure, enabling query capabilities, pipeline analytics, and run management through simple conversation. Explore current features, engineering decisions, and future roadmap for this timely addition to the rapidly evolving MCP ecosystem.
Discover how ZenML implements the llms.txt standard to make ML documentation more accessible to both AI assistants and humans. Learn about our modular approach using specialized documentation files, practical integration with AI development tools, and how this structured format enhances the developer experience across different context window sizes.
ZenML 0.70.0 has launched with major improvements but requires careful handling during upgrade due to significant database schema changes. Key highlights include enhanced artifact versioning with batch processing capabilities, improved scalability through reduced server requests, unified metadata management via the new log_metadata method, and flexible filtering with the new oneof operator. The release also features expanded documentation covering finetuning and LLM/ML engineering resources. Due to the database changes, users must back up their data and test the upgrade in a non-production environment before deploying to production systems.
The ZenML team has addressed a security finding in ZenML Pro's role management system, reported by JFrog Security Research team. This update provides important information for users regarding role-based access controls and recommended actions
Learn how to leverage caching, parameterization, and smart infrastructure switching to iterate faster on machine learning projects while maintaining reproducibility.
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