Supercharge Open Source ML Workflows with ZenML And Skypilot
The combination of ZenML and SkyPilot offers a robust solution for managing ML workflows.
Aug 30, 20245 mins
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.
Discover the technical challenges and solutions in developing DAG visualization and stack management for ZenML's VSCode extension.
An overview of MLOps principles, implementation strategies, best practices, and tools for managing machine learning lifecycles.
ZenML's new direction: Simplifying infrastructure connections for enhanced MLOps.
