20 posts in this category
Discover why production teams are treating agentic workflows as MLOps evolution, not revolution—plus how ZenML achieved 200x performance improvements for enterprise ML operations. Real insights from 130+ MLOps engineers on building reliable AI systems.
Our monthly roundup: new features with 0.80.0 release, more new models, and an MCP server for ZenML
ZenML 0.80.0 transforms tenant structures into workspace/project hierarchies with advanced RBAC for Pro users, while enhancing tagging, resource filtering, and dashboard design. Open-source improvements include Kubernetes security upgrades, SkyPilot integration, and significantly faster CLI operations. Both Pro and OSS users benefit from dramatic performance optimizations, GitLab improvements, and enhanced build tracking.
Our monthly roundup: Hamza visits the US, a new course built on ZenML and why workflows are better than autonomous agents!
ZenML 0.75.0 introduces dashboard enhancements that allow users to create and update stack components directly from the dashboard, along with improvements to service connectors, model artifact handling, and documentation. This release streamlines ML workflows with better component management capabilities, enhanced SageMaker integration, and critical fixes for custom flavor components and sorting logic.
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.
Our monthly roundup: AI Infrastructure Summit insights, new experiment comparison tools, and a deep dive into AI Engineering roles
ZenML's new Experiment Comparison Tool brings powerful experiment tracking capabilities to your ML pipelines. Compare up to 20 pipeline runs simultaneously through intuitive tabular and parallel coordinates visualizations, helping teams derive actionable insights from their pipeline metadata. Now available in the Pro tier dashboard.
ZenML 0.71.0 features the Modal Step Operator for fast, configurable cloud execution, dynamic artifact naming, and enhanced visualizations. It improves API token management, dashboard usability, and infrastructure stability while fixing key bugs. Expanded documentation supports advanced workflows and big data management.
ZenML 0.68.0 introduces several major enhancements including the return of stack components visualization on the dashboard, powerful client-side caching for improved performance, and a streamlined onboarding process that unifies starter and production setups. The release also brings improved artifact management with the new `register_artifact` function, enhanced BentoML integration (v1.3.5), and comprehensive documentation updates, while deprecating legacy features including Python 3.8 support.
This release incorporates updates to the SageMaker Orchestrator, DAG Visualizer, and environment variable handling. It also includes Kubernetes support for Skypilot and an updated Deepchecks integration. Various other improvements and bug fixes have been implemented across different areas of the platform.
ZenML's latest release 0.66.0 adds support for Python 3.12, removes some dependencies for a slimmer Client package and adds the ability to view all your pipeline runs in the dashboard.
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.
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.
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.
Infrastructure-as-code meets MLOps: Terraform modules for deploying ML infrastructure on AWS, GCP, and Azure on the Hashicorp registry.
Now you can easily connect AWS, GCP, and Azure cloud providers with ZenML directly with an easy wizard in the dashboard.
Streamline your machine learning platform with ZenML. Learn how ZenML's 1-click cloud stack deployments simplify setting up MLOps pipelines on AWS, GCP, and Azure.
The 0.40.0 release introduces a completely reworked interface for developing your ZenML steps and pipelines. It makes working with these components much more natural, intuitive, and enjoyable.
ZenML makes it easy to setup training pipelines that give you all the benefits of cached steps.
