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
A new ZenML newsletter featuring Istanbul cooking adventures, faster docker builds, and more
Comparing Airflow, Dagster, and Prefect: Choosing the right orchestration tool for your data workflows.
The combination of ZenML and SkyPilot offers a robust solution for managing ML workflows.
MLOps on Google Cloud Platform streamlines machine learning workflows using Vertex AI and ZenML.
ZenML's latest release 0.65.0 enhances MLOps workflows with single-step pipeline execution, AzureML SDK v2 integration, and dynamic model versioning. The update also introduces a new quickstart experience, improved logging, and better artifact handling. These features aim to streamline ML development, improve cloud integration, and boost efficiency for data science teams across local and cloud environments.
Two open-source contributors describe how they built a new onboarding experience for the ZenML Quickstart native to VS Code.
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.
Playing around with some genAI services and tools to create a story and comic that showcases the journey of MLOps adoption for a small team.
Cloud Composer (Airflow) vs Vertex AI (Kubeflow): How to choose the right orchestration service on GCP based on your requirements and internal resources.
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.
