The Unified AI Stack: Pipelines for Models and Agents

​AI teams today are managing two parallel worlds: classical ML models that drive predictions and LLM agents that orchestrate real-time reasoning. Both face the same production challenges — fragmented infrastructure, inconsistent tracking, and difficult rollbacks.

​With the newest ZenML release, Pipeline Deployments bring these worlds together. They turn any pipeline into a persistent, real-time service with built-in lifecycle management, observability, and governance — no custom serving framework required.

​Join the ZenML team for a live walkthrough of what’s new and what it means for your stack:

• ​🚀 Deploy any pipeline — from scikit-learn to LangGraph — as a managed, callable service

• ​🧩 Keep your infrastructure consistent across agents and models

• ​🔁 Roll back safely with immutable deployment snapshots

• ​🧠 Trace, monitor, and debug every invocation in the ZenML dashboard

• ​🌐 Serve both backend APIs and frontends from a single deployment

​This release marks a major step toward the unified AI stack — where agents and models share the same deployment, observability, and control layer.

​👉 Save your seat to see how Pipeline Deployments simplify AI infrastructure and make production-ready systems easier to build and manage.