ZenML Blog

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.

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.

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.

A technical deep dive into the performance optimizations that improved ZenML's throughput by 200x

Manual EU AI Act compliance is unmanageable. This credit scoring pipeline shows how ZenML transforms regulatory requirements into automated workflows—from bias detection and risk assessment to human oversight gates and Annex IV documentation.

Traditional banks face growing pressure to deploy machine learning rapidly while meeting strict regulatory requirements. This blog post explores how modern MLOps practices, like automated data lineage, validation testing, and model observability can help financial institutions bridge the gap. Featuring real-world insights from NatWest and an open-source ZenML pipeline, it offers a practical roadmap for compliant, scalable AI deployment.

Future-proof your ML operations by building portable pipelines that work across multiple platforms instead of forcing standardization on a single solution.

An in-depth analysis of retail MLOps challenges, covering data complexity, edge computing, seasonality, and multi-cloud deployment, with real-world examples from major retailers like Wayfair and Starbucks, and practical solutions including ZenML's impact in reducing deployment time from 8.5 to 2 weeks at Adeo Leroy Merlin.

Discover how to optimize GPU utilization in Kubernetes environments by integrating NVIDIA's KAI Scheduler with ZenML pipelines, enabling fractional GPU allocation for improved resource efficiency and cost savings in machine learning workflows.