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.
Master cloud-based LLM finetuning: Set up infrastructure, run pipelines, and manage experiments with ZenML's Model Control Plane for Microsoft's latest Phi model.
We compare ZenML with Apache Airflow, the popular data engineering pipeline tool. For machine learning workflows, using Airflow with ZenML will give you a more comprehensive solution.
ZenML's latest release 0.64.0 streamlines MLOps workflows with notebook integration for remote pipelines, optimized Docker builds, AzureML orchestrator support, and Terraform modules for cloud stack provisioning. These updates aim to speed up development, ease cloud deployments, and improve efficiency for data science teams.
Cloud Composer (Airflow) vs Vertex AI (Kubeflow): How to choose the right orchestration service on GCP based on your requirements and internal resources.
Recent releases of ZenML’s Python package have included a better way to deploy machine learning infrastructure or stacks, new annotation tool integrations, an upgrade of our Pydantic dependency and lots of documentation improvements.
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