gcp

Discover how ZenML's Service Connectors solve one of MLOps' most frustrating challenges: credential management. This deep dive explores how Service Connectors eliminate security risks and save engineer time by providing a unified authentication layer across cloud providers (AWS, GCP, Azure). Learn how this approach improves developer experience with reduced boilerplate, enforces security best practices with short-lived tokens, and enables true multi-cloud ML workflows without credential headaches. Compare ZenML's solution with alternatives from Kubeflow, Airflow, and cloud-native platforms to understand why proper credential abstraction is the unsung hero of efficient MLOps.

ZenML 0.74.0 introduces key cloud provider features including SageMaker pipeline scheduling, Azure Container Registry implicit authentication, and Vertex AI persistent resource support. The release adds API Tokens for secure, time-boxed API authentication while delivering comprehensive improvements to timezone handling, database performance, and Helm chart deployments.

Why use ZenML alongside AWS / GCP / Azure MLOps platforms? Let's dive into why ZenML complements and enhance existing cloud MLOps infrastructure.

MLOps on Google Cloud Platform streamlines machine learning workflows using Vertex AI and ZenML.

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.

Infrastructure-as-code meets MLOps: Terraform modules for deploying ML infrastructure on AWS, GCP, and Azure on the Hashicorp registry.

Master cloud-based LLM finetuning: Set up infrastructure, run pipelines, and manage experiments with ZenML's Model Control Plane for Meta's latest Llama model.