Server & SDK

Changelog for ZenML OSS and ZenML UI.

Stay up to date with the latest features, improvements, and fixes in ZenML OSS.

0.94.6 (2026-06-02)

See what's new and improved in version 0.94.6.

Infrastructure & Deployment Improvements

Enhanced GKE Private Cluster Support: Fixed GCP service connector failures when connecting to private GKE clusters that use Google's DNS-based control plane endpoint. ZenML now connects using the same method as gcloud container clusters get-credentials --dns-endpoint, ensuring reliable access to private clusters. PR #4856

Fixed

Docker Build Requirements: Resolved an issue where ZenML would fail when no container engine was available, even when users explicitly set skip_build=True in their DockerSettings. The build checksum computation now correctly respects the skip_build flag. PR #4879

View full release on GitHub

0.94.5 (2026-05-29)

See what's new and improved in version 0.94.5.

🚀 Live Event Streaming for Pipeline Runs

You can now stream custom events in real-time from your running pipelines! Call zenml.streaming.publish() from inside any step or dynamic pipeline to push events that can be consumed via Server-Sent Events (SSE). Enable this feature by setting stream_broker_implementation_source in your server configuration. The initial implementation includes a Redis-based broker with automatic catch-up, gap signaling, and idle cleanup. PR #4804

📊 Pipeline Run Statistics Endpoint

A new POST /api/v1/runs/statistics endpoint lets you query aggregated metrics across your pipeline runs. Group by status, pipeline, stack, user, time buckets (hour/day/week/month), metadata values, tags, and more. Calculate averages, sums, min/max over duration, step counts, cached steps, output artifacts, or custom numeric metadata. Perfect for building dashboards and analytics. PR #4860

🎯 Richer Weights & Biases Integration

The W&B experiment tracker now automatically adds ZenML pipeline and step metadata to your W&B runs, groups runs by pipeline execution, and records W&B identifiers back to ZenML step metadata. You can now configure custom groups, job types, run configs, explicit or deterministic run IDs, resume behavior, and pass through additional wandb.init kwargs for complete control over your experiment tracking. PR #4838

🤖 Agentic Human-in-the-Loop Pipeline Example

A new example demonstrates building dynamic agentic pipelines with human approval gates. The example shows how to plan agent tasks, fan them out with step.map(), summarize results, pause execution with zenml.wait() for human review, and branch the final action based on the decision. Includes clean lineage tracking with Annotated step outputs. PR #4849

📝 Structured Logging and OpenTelemetry Instrumentation

The ZenML server now supports structured logging with OpenTelemetry instrumentation. Configure console output with the new ZENML_CONSOLE_LOGGING_FORMAT environment variable, choosing between console, json, or text formats. Server logs use a clean structured layout with timestamps, levels, logger context, and optional JSON fields. PR #4781

🛠️ Enhanced Developer Experience

String Type Annotations: Step and pipeline definitions now support string annotations, either explicitly quoted or via from __future__ import annotations. PR #4843
Build Cache Mounting: Python package installations during Docker image building can now mount a build cache for faster builds. PR #4820
Improved Wait Condition Input: When resolving wait conditions interactively with string schemas, you can now input raw strings without quotes—ZenML detects and handles this automatically. PR #4845
Better Logging Control: Restored custom console log formatting for non-DEBUG output, added step-name prefixes in terminal output during execution (disable with ZENML_DISABLE_STEP_NAMES_IN_LOGS=true), and kept stored logs clean and unformatted. The ZENML_LOGGING_FORMAT variable is now deprecated in favor of ZENML_CONSOLE_LOGGING_FORMAT. PR #4851

🎨 Dashboard Improvements

Improved scrolling behavior for the pipeline timeline view. PR #1053

Fixed

Pipeline runs now properly fail when the user pipeline function cannot be imported (e.g., due to missing packages), instead of staying in a running state indefinitely. PR #4832
Keyboard interrupts during active wait conditions are now handled more gracefully, preventing unclear error messages when the server transitions to STOPPED status. PR #4835
Fixed a copy-paste bug in the user update endpoint where admins couldn't activate or deactivate user accounts—the code was incorrectly writing is_admin instead of active to the safe update object. PR #4839

View full release on GitHub

0.94.4 (2026-05-12)

See what's new and improved in version 0.94.4.

New Databricks Step Operator

You can now run individual pipeline steps on Databricks using the new Databricks step operator PR #4648. This is useful when you want specific steps to execute in the Databricks runtime while the rest of your pipeline uses a different orchestrator. The Databricks orchestrator also now supports optional tag settings to label jobs and cluster resources for cost tracking, ownership, and governance.

Nested Dynamic Pipelines

Dynamic pipelines can now be nested, allowing you to call one dynamic pipeline from within another PR #4775. This enables more modular and reusable pipeline designs.

Enhanced Run:AI Training Workload Configuration

The Run:AI step operator now supports advanced training workload settings PR #4780, including:

Multiple mount types (PVC, ConfigMap, Secret, NFS, S3, HostPath)
Workload templates via workload_template_id
Security context settings (UID/GID, non-root execution, seccomp, capabilities)
Port declarations and external URL exposure
Training workload parallelism and completions

Improved Kubernetes Job Failure Diagnostics

When dynamic pipeline jobs fail due to system issues (such as OOM kills), ZenML now provides richer diagnostic information PR #4800. This makes it easier to understand why Kubernetes terminated your pods.

Better Kubernetes Label Handling

Kubernetes string handling has been improved with separate sanitization for DNS-style names/keys and looser label-value rules for metadata like run, pipeline, and step IDs PR #4756. This makes it easier to navigate through runs in Kubernetes.

Increased Secret Size Limit

The maximum allowed size for ZenML secrets stored in the SQL secrets store has been increased to 64KB PR #4769. The limit applies to the combined size of all keys and values in a secret object.

Dashboard: Parent Run Display

The dashboard now displays parent run information in the run details view when available PR #1050.

Fixed

Signal handling during step execution: Signal handlers are now properly unregistered after step execution, preventing strange errors when running many sync steps or interrupting pipelines PR #4784. Dynamic pipeline steps running in isolated environments or step operators are no longer affected by signal handling from the orchestrator.
RBAC performance: Fixed redundant RBAC permission checks during response model dehydration PR #4797. Previously, when permissions were already prefetched and denied, additional RBAC requests were sent unnecessarily for each sub-model.
Keyword-only arguments in steps: Step functions can now use keyword-only arguments without causing failures PR #4798.
Kubernetes orchestrator settings: Fixed step pod configuration by replacing legacy hardcoded orchestrator.kubernetes lookups with proper orchestrator.get_settings(...) calls PR #4803. Step pods now correctly apply orchestrator settings from canonical component keys while maintaining backward compatibility.

View full release on GitHub

0.94.3 (2026-04-24)

See what's new and improved in version 0.94.3.

🚀 New Features

Podman Support & Container Engine Abstraction

ZenML now supports Podman as an alternative to Docker for container image management. A new ContainerEngine abstraction has been introduced to make it easier to work with different OCI-compatible container runtimes. This gives you more flexibility in choosing your container tooling, especially in environments where Docker isn't available or preferred. PR #4651

Resource Pools (Pro only)

Introducing Resource Pools - a new way to manage and organize compute resources in ZenML. This feature includes full SDK methods, CLI commands, and API endpoints to create and manage resource pools and their associated objects, giving you better control over resource allocation across your ML workflows. PR #4465

Platform Event Triggers (Pro only)

You can now set up event-based triggers that automatically execute downstream workflows based on ZenML platform events, such as when a pipeline run completes. This enables powerful automation patterns and reactive workflows without manual intervention. Full backend management including CLI and SDK support is now available. PR #4692

Server-Side Pipeline Replays (Pro only)

Pipeline runs can now be replayed from the server with advanced capabilities:

Skip specific steps - steps won't re-execute even if cache was disabled or inputs changed
Override step input artifacts - go beyond parameter overrides and replace inputs that came from upstream steps in the original run

This gives you fine-grained control over re-executing parts of your pipelines. PR #4716

Multiple Components Per Stack Type

Stacks can now include multiple components of the same type for Alerters, Step Operators, and Experiment Trackers. You can designate one as the default while having others available for specific use cases, providing more flexibility in stack composition. PR #4671

Kubernetes Gateway API Support

The ZenML server can now be exposed via Gateway API HTTPRoute resources as an alternative to Ingress. Both Ingress and Gateway can be enabled simultaneously, making it easy to migrate from Ingress to Gateway API with zero downtime. PR #4726

✨ Enhancements

Improved Trigger Management

Triggers now track per-snapshot dispatch state showing whether the last run succeeded, was skipped due to concurrency policy, or failed
Failed dispatches store richer error context including message, type, severity, stack trace, and timestamps
New acknowledge flow lets you clear stored error context without re-attaching triggers or changing configuration

PR #4743

Schedule Stop Criteria

Schedules now support a max-runs limit to automatically stop after a specified number of executions per snapshot, giving you better control over scheduled pipeline runs. PR #4752

Non-Blocking Concurrent Steps

Dynamic pipelines with concurrent steps now execute more efficiently. Waiting for step inputs happens asynchronously instead of blocking the main thread, significantly improving pipeline execution performance when using concurrent steps. PR #4699

🐛 Fixed

Run status updates: Fixed a bug where pipeline runs weren't properly marked as failed when a step pod failed after initially reporting success PR #4747
Secret visibility updates: You can now properly update secrets from private to public using the CLI or client PR #4755
Logs for restarted runs: Removed the unique constraint from logs schema to allow log entries for repeated executions of the same pipeline run PR #4729
Air-gapped performance: Flavor model generation no longer calls PyPI to resolve ZenML versions, improving performance in air-gapped environments PR #4744

View full release on GitHub

0.94.2 (2026-04-08)

See what's new and improved in version 0.94.2.

🎨 Dashboard Enhancements

The ZenML dashboard now includes a Run Summary View that provides a comprehensive overview of your pipeline runs at a glance PR #1029. Timeline rows now automatically resize for better visualization of your pipeline execution history PR #1028.

🔧 Pipeline & Trigger Improvements

Artifact Name Substitutions for Dynamic Pipelines: You can now use artifact name substitutions in dynamic pipelines, making it easier to reference and manage artifacts programmatically PR #4668.

Enhanced Trigger Configuration: Triggered runs now follow a cleaner snapshot.source pattern. You can provide a configuration object to customize parameters for all triggered runs in an attachment, giving you more control over automated pipeline executions PR #4610.

⚠️ Deprecation Notice

Helm Chart Configuration Update: The top-level zenml: values key in the ZenML Helm chart has been renamed to server: PR #4637. Your existing values files will continue to work — the chart automatically merges both keys for backwards compatibility. If both keys are present, zenml: takes precedence for overlapping fields. The zenml: key is deprecated and will be removed in a future release; we recommend migrating to server: in your Helm configurations.

Fixed

Schedule Management: Fixed issues where deletion by ID prefix wasn't working for archived schedules, and reusing an archived schedule name was incorrectly blocked. Documentation has been updated to clarify schedule behavior post-deletion, including archived schedule renaming, hard deletion guidelines, and discovery of archived objects PR #4641.
Input Artifact Handling: Resolved a bug where a list of input artifacts containing a single item was incorrectly treated as a scalar input artifact, causing materializer type incompatibility errors PR #4679.
HTTP Request Optimization: The system now dynamically reduces chunk size when fetching many steps to avoid HTTP 414 (URI Too Long) errors PR #4553.
Dashboard Deployment Stability: Fixed a template loading issue in the FastAPI deployment app's dashboard that could cause TypeError: unhashable type: 'dict' on certain environments (e.g., Kubernetes with newer Jinja2 versions), preventing the server from starting when the dashboard was enabled PR #4649.

View full release on GitHub

0.94.1 (2026-03-19)

See what's new and improved in version 0.94.1.

🎯 Pipeline Execution Control

Pause and Resume Pipeline Runs: Introduced zenml.wait(...) to pause dynamic pipelines while waiting for external inputs, automatically freeing resources until the input is provided. Runs can be resumed automatically (when using remote orchestrators with snapshot support) or manually via zenml pipeline runs resume <ID>. PR #4588
Override Step Inputs on Replay: You can now override step inputs when replaying a pipeline run, giving you more flexibility to rerun pipelines with different data. PR #4590

🔧 Materializers and Data Handling

Dataclass Materializer: Added a built-in materializer for JSON-serializable dataclasses, making it easier to pass structured data between steps. PR #4600
LakeFS Data Versioning Example: New example demonstrating the "pass references, not data" pattern for terabyte-scale datasets. ZenML steps exchange lightweight LakeFS pointers while actual data stays in LakeFS, accessed via its S3-compatible gateway. PR #4559

☁️ Infrastructure and Deployment

Helm Environment Variable Overrides: Environment variables specified in zenml.environment, zenml.secretEnvironment, and worker deployment configurations can now override computed settings from the Helm chart. PR #4595
Secret Environment Variables in Helm: Added support for injecting secret environment variables into the ZenML server deployment via Helm without committing secrets to values.yaml, enabling better GitOps workflows. PR #4606
Docker Build Arguments: Build arguments defined in DockerSettings are now properly declared with ARG instructions in auto-generated Dockerfiles. PR #4612

🔐 Authentication and Credentials

Improved GCP Credentials Refresh: Implemented native GCP credentials refresh using service connector logic, replacing periodic expiration checks for more reliable OAuth2 credential handling. PR #4527

📊 Pipeline Configuration

Step Parameter Schema Storage: Step parameter specifications are now stored for better schema validation when triggering pipeline snapshots. PR #4591

Fixed

Exception Handling for Isolated Steps: Isolated steps in dynamic pipelines now raise the correct exception types instead of always wrapping them in RuntimeError, making error handling more intuitive and consistent. PR #4589
Kubernetes API Request Timeout: Fixed typing issue in the Kubernetes orchestrator and step operator to ensure api_request_timeout is properly applied as an integer value. PR #4605
Client-Server Compatibility: Resolved compatibility issue with clients <=0.92.0 by not passing both pipeline run ID and step run ID to logs requests. PR #4614
Dynamic Pipeline Monitoring: Fixed dictionary size change error that occurred when the main thread modified the _steps_to_monitor dictionary while the monitoring thread was iterating over it. PR #4619
Step Config Template Parameters: Step parameters defined in config templates now correctly take precedence over function default values in dynamic pipelines. PR #4624

📦 Dependencies

Updated Pydantic to version 2.12.5. PR #4552

View full release on GitHub

0.94.0 (2026-03-04)

See what's new and improved in version 0.94.0.

Breaking Changes

Old endpoints and client methods for legacy triggers, actions and event sources have been removed. This shouldn't affect you unless you explicitly used those endpoints or methods in your code.
Custom step operator flavors must implement new submit_step and get_step_status methods to work with dynamic pipelines. The legacy launch method will only work in static pipelines as a fallback. The Spark step operator is not yet compatible with dynamic pipelines. PR #4515

🚀 New Integrations

Run:AI Step Operator: ZenML now supports running individual pipeline steps on Run:AI clusters with fractional GPU allocation, enabling more efficient resource utilization for ML workloads. PR #4439

✨ New Features

Step and Pipeline Replays: You can now replay existing step or pipeline runs with the same inputs and configuration. When replaying a pipeline run, you can specify which steps to skip and reuse from the original run. A debug mode is also available to run replays on your active stack with a local orchestrator. PR #4456
Triggers and Native Schedules (PRO): Introduced the Trigger concept for automated pipeline execution. The first supported trigger type is Schedules, which offers lifecycle management, automatic synchronization with orchestrators, and centralized management across stacks. PR #4482
Step Run Filtering by Version: Added the ability to filter step runs by version, making it easier to track and manage specific versions of your pipeline steps. PR #4518

🔧 Improvements

Enhanced Dynamic Pipeline Monitoring: Improved the execution and monitoring of isolated steps in dynamic pipelines. Step submission is now separated from monitoring, preventing thread blocking during step execution. PR #4369
SkyPilot Integration Update: Updated the SkyPilot integration to support version 0.11.x, including migration to the new async API and support for new resource settings. PR #4462
Kubernetes Retry Configuration: Added configurable timeout options for Kubernetes orchestrator and step operator API calls, ensuring proper retry behavior and preventing unnecessary hanging. PR #4525
Git Submodule Support: Code archives now include files from git submodules when uploading to the artifact store, ensuring complete code tracking for repositories with submodules. PR #4496

Fixed

Fixed handling of variadic keyword arguments in pipeline functions, ensuring they are properly flattened before being passed to the pipeline. PR #4528
Fixed Azure integration dependencies by limiting the azure-mgmt-resource version to avoid compatibility issues with the 25.0.0 package split. PR #4516
Fixed handling of external artifacts with None values and reduced chunk size when fetching many step runs to avoid URI too large errors. PR #4551

View full release on GitHub

0.93.3 (2026-02-19)

See what's new and improved in version 0.93.3.

Performance Improvements

This release includes significant performance optimizations for the ZenML server, particularly when handling large-scale deployments:

Improved database query efficiency: Rewrote filtering queries to eliminate unnecessary sorting during item counting, removed inefficient DISTINCT statements on multiple columns, and optimized OR subqueries for better database performance at scale. PR #4449
Enhanced API transaction management: Moved cleanup of expired transactions to an independent background thread that runs periodically, significantly improving API response times especially for large payloads like pipeline snapshots with many steps. PR #4453

Logging Enhancements

Logging capabilities have been expanded with new features and improvements:

Added new create and update endpoints for logs with support for UUIDs in StepRunRequest and PipelineRunRequest
Introduced workspace ID and name to pipeline run log metadata (with backward compatibility)
Added zenml.event.type to error messages for better context tracking
Introduced environment variable to manage maximum log entries per request
Fixed inconsistent metadata key formatting (standardized zenml. prefix) PR #4405, PR #4467

Dashboard Updates

Added elapsed time display to step nodes in the DAG visualization for better pipeline monitoring PR #994

Fixed

Critical data loss bug: Fixed a critical issue in download_artifact_files_from_response that caused silent data corruption when downloading artifacts larger than 8KB. The bug resulted in up to 98%+ data loss for large artifacts by only preserving the last chunk of data. PR #4422
ZenML Pro migration: Fixed an issue where cookies from local user accounts persisted after migrating a ZenML OSS server to ZenML Pro via organization enrollment, preventing access to migrated resources in the UI. The server now properly rejects these stale cookies. PR #4473
UV-only environments: Fixed pipeline run crashes in environments using only uv without pip installed. ZenML now falls back to uv pip freeze when pip freeze is unavailable for collecting environment metadata. Also added UV_FREEZE as an export method for Docker builds. PR #4484
Kubernetes credential expiration: Fixed an issue where Kubernetes credentials issued by service connectors expired while monitoring long-running jobs, causing monitoring failures. Credentials are now properly refreshed during job monitoring. PR #4493
Improved CLI messaging when attempting to activate a stack without proper permissions

View full release on GitHub

0.93.2 (2026-01-29)

See what's new and improved in version 0.93.2.

🎨 Dashboard Enhancements

The ZenML Dashboard now provides better visibility into your pipelines and infrastructure:

Download Pipeline Code: You can now download the code used for a pipeline snapshot directly from the dashboard. A new Download button appears in the "Code Path" section on both the Pipeline Run details page and the Step details sheet, making it easy to retrieve and review the exact code that was executed. PR #4401, PR #989
Exception Information Display: When dynamic pipeline runs fail, the dashboard now displays detailed exception information, helping you quickly diagnose and troubleshoot issues. PR #4395, PR #990
Stack & Component Labels: Labels attached to stacks and components are now visible in the dashboard, making it easier to organize and identify your infrastructure resources. PR #992

🔄 Dynamic Pipeline Improvements

Dynamic pipelines are now more robust and easier to work with:

Proper Environment Configuration: The pipeline environment is now correctly set while running the entrypoint function of dynamic pipelines, ensuring consistent behavior across different execution contexts. PR #4420

🤖 Developer Experience

Claude Code Plugin: A new ZenML Quick Wins skill for Claude Code helps you implement MLOps best practices directly in your AI-assisted coding workflow. The plugin is available through the Claude Code plugin marketplace and includes comprehensive documentation for multiple AI coding tools. PR #4426

Fixed

🚀 Performance & Scalability

Artifact Download Fix: Resolved an issue where artifact version downloads were failing due to incorrect RBAC checks on the download endpoint. PR #4401

View full release on GitHub

0.93.1 (2026-01-14)

See what's new and improved in version 0.93.1.

🎛️ Schedule Management Enhancements

You can now pause and resume pipeline schedules directly from the CLI, giving you better control over automated pipeline executions. Use the new commands to activate or deactivate schedules on demand:

zenml pipeline schedule deactivate <schedule_id>
zenml pipeline schedule activate <schedule_id>

Currently available for the Kubernetes orchestrator. PR #4328

Schedules now support archiving as a soft-delete operation. When you delete a schedule, it's archived instead of permanently removed, preserving historical references so your pipeline runs maintain their schedule associations. PR #4339

🖥️ Dashboard Improvements

Stack Management: You can now update existing stacks directly from the UI without having to delete and recreate them. A new dedicated stack update page lets you add or replace stack components (orchestrators, artifact stores, container registries, etc.) efficiently. PR #978

Step Cache Management: View and manage step cache expiration directly from the step details panel. The cache expiration field shows when a step's cache will expire (or "Never" if no expiration is set), with expired caches clearly marked. You can also manually invalidate a step's cache with a single click. PR #976

Enhanced Logs Experience: Pipeline runs now have a dedicated logs page with a sidebar for navigating between run-level and step logs. The new logs viewer features virtualized rendering for better performance with large outputs, search and filtering capabilities, and step duration display. PR #985

⚡ Performance & Reliability

Kubernetes Orchestrator Improvements: The Kubernetes orchestrator now runs more efficiently with configurable DAG runner workers, optimized cache candidate fetching, and better error handling for failed step pods. PR #4368

Database Backup Speed: A new mydumper/myloader backup strategy delivers dramatically faster operations:

30x faster database backups
2.5x faster database restores
10x lower storage space requirements

PR #4358

🚀 Orchestrator Features

AzureML Dynamic Pipelines: Dynamic pipelines are now fully supported on the AzureML orchestrator, expanding your options for flexible pipeline execution. PR #4363

Kubernetes Init Container Templating: When configuring init containers for the Kubernetes orchestrator, you can now use an "{{ image }}" placeholder that will be automatically replaced with the actual orchestration/step container image. PR #4361

Fixed

Fixed per-step compute settings not being applied correctly PR #4362
Fixed database migration script to handle pipelines with zero runs PR #4360
Fixed working directory in dynamic pipeline containers (was /zenml instead of /app) PR #4379
Fixed pipeline run status updates in CONTINUE_ON_FAILURE execution mode PR #4379
Fixed component setting shortcut keys when running snapshots PR #4379
Improved error messages during source validation and for string type annotations PR #4359
Fixed log storage in Kubernetes orchestrator by propagating context vars to DAG runner threads PR #4359
Pipeline source code now included for runs triggered by snapshots/deployments PR #4359

View full release on GitHub

0.93.0 (2025-12-16)

See what's new and improved in version 0.93.0.

Breaking Changes

The logging system has been completely redesigned with a new log store abstraction that now captures stdout, stderr, and all logger outputs more comprehensively. If you have custom integrations that relied on the previous logging behavior or accessed logs directly from the artifact store, you may need to update your code to use the new log store APIs. PR #4111
The REST API endpoint /api/v1/pipelines/<ID>/runs has been removed. Use /api/v1/runs?pipeline_id=<ID> instead to fetch runs for a specific pipeline. PR #4350
The logs field has been removed from the response models of pipeline runs and steps. Additionally, RBAC checks for fetching logs, downloading artifacts, and visualizations have been tightened. If you were accessing logs through these response models, you will need to use the dedicated log fetching endpoints instead. PR #4347

Enhanced CLI Experience

The ZenML CLI now provides a more flexible and user-friendly experience with improved table rendering and output options. Tables are now more aesthetically pleasing with intelligent column sizing, and you can pipe CLI output in multiple formats (JSON, YAML, CSV, TSV) by properly separating stdout and stderr streams. This makes it easier to integrate ZenML commands into scripts and automation workflows. PR #4241

Dynamic Pipeline Support

Dynamic pipelines can now be deployed and run with the local Docker orchestrator, including support for asynchronous execution. This expands the flexibility of local development and testing workflows, allowing you to leverage dynamic pipeline patterns without requiring cloud infrastructure. PR #4294, PR #4300

Pipeline Run Tracking

Each pipeline run now includes an index attribute that tracks its position within the pipeline's execution history, making it easier to identify and reference specific runs in a sequence. PR #4288

Orchestrator Health Monitoring

The Kubernetes orchestrator now includes enhanced health monitoring capabilities with configurable heartbeat thresholds. Steps that become unhealthy are preemptively stopped, and pipeline tokens are automatically invalidated when pipelines enter an unhealthy state, improving reliability and resource management. PR #4247

New Integrations

Alibaba Cloud Storage: Added support for Alibaba Cloud OSS as an artifact store, expanding ZenML's cloud storage options. PR #4289
Generic OTEL Log Store: Introduced a new log store flavor that can connect to any OTEL/HTTP/JSON compatible log intake endpoint, enabling integration with a wider range of observability platforms. PR #4309

Azure ML Enhancements

The AzureML orchestrator and step operator now support shared memory size configuration, giving you more control over resource allocation for your workloads. PR #4334

Fixed

MLflow Experiment Tracker: Fixed crashes when attempting to resume non-existent runs on Azure ML. The tracker now validates cached run IDs and gracefully creates new runs when necessary. PR #4227
Kubernetes Service Connector: Resolved failures in the ZenML server related to the Kubernetes service connector caused by incompatible urllib3 and kubernetes client library versions. PR #4312
Datadog Log Store: Improved log fetching with proper pagination support, handling the Datadog API's 1000-log limit per request through cursor-based iteration. PR #4314
Deployment Log Flushing: Eliminated blocking behavior when flushing logs during deployment invocations, preventing potential hangs at pipeline completion. PR #4354

View full release on GitHub

0.92.0 (2025-12-02)

See what's new and improved in version 0.92.0.

Dynamic Pipeline Support Expansion

This release significantly expands support for dynamic pipelines across multiple orchestrators:

AWS Sagemaker Orchestrator: Added full support for running dynamic pipelines with seamless transition from existing settings and faster execution through direct use of training jobs. PR #4232
Vertex AI Orchestrator: Dynamic pipelines are now fully supported on Google Cloud's Vertex AI platform. PR #4246
Kubernetes Orchestrator: Improved dynamic pipeline handling by eliminating unnecessary pod restarts. PR #4261
Snapshot Execution: For Pro users, the new release enabled running snapshots of dynamic pipelines from the server with support for specifying pipeline parameters. PR #4253

Improved

Enhanced step.map(...) and step.product(...) to return a single future object instead of a list of futures, simplifying the API for step invocations. PR #4261
Improved placeholder run handling to prevent potential issues in dynamic pipeline execution. PR #4261
Added better typing for Docker build options with a new class to help with conversions between SDK and CLI. PR #4262

GCP Image Builder Regional Support

Added regional location support to the GCP Image Builder, allowing you to specify Cloud Build regions for improved performance and compliance:

Optional location parameter for specifying Cloud Build region
Uses regional Cloud Build endpoint ({location}-cloudbuild.googleapis.com) when location is set
Maintains backward compatibility with global endpoint as default
Includes input validation for location parameter

PR #4268

Integration Updates

Evidently Integration: Updated to version >=0.5.0 to support NumPy 2.0, resolving compatibility issues when installing packages requiring NumPy 2.0+ alongside ZenML. PR #4243

View full release on GitHub

0.91.2 (2025-11-19)

See what's new and improved in version 0.91.2.

Kubernetes Deployer

Deploy your pipelines directly on Kubernetes
Full integration with Kubernetes orchestrator

Learn more | PR #4127

MLflow 3.0 Support

Added support for the latest MLflow version
Improved compatibility with modern MLflow features

PR #4160

S3 Artifact Store Fixes

Fixed compatibility with custom S3 backends
Improved SSL certificate handling for RestZenStore
Enhanced Weights & Biases experiment tracker reliability

UI Updates

Remove Video Modal (#943)
Update Dependencies (CVE) (#945)
Adjust text-color (#947)
Sanitize Dockerfile (#948)

Fixed

S3 artifact store now works with custom backends (#4186)
SSL certificate passing for RestZenStore (#4188)
Weights & Biases tag length limitations (#4189)

View full release on GitHub

0.91.1 (2025-11-11)

See what's new and improved in version 0.91.1.

Hugging Face Deployer

Deploy pipelines directly to Hugging Face Spaces
Seamless integration with Hugging Face infrastructure

Learn more | PR #4119

Dynamic Pipelines (Experimental)

Introduced v1 of dynamic pipelines
Early feedback welcome for this experimental feature

Read the documentation | PR #4074

Kubernetes Orchestrator Enhancements

Container security context configuration
Skip owner references option
Improved deployment reliability

UI Updates

Display Deployment in Run Detail (#919)
Announcements Widget (#926)
Add Resize Observer to HTML Viz (#928)
Adjust Overview Pipelines (#914)
Fix Panel background (#882)
Input Styling (#911)
Display Schedules (#879)

Improved

Enhanced Kubernetes orchestrator with container security context options (#4142)
Better handling of owner references in Kubernetes deployments (#4146)
Expanded HashiCorp Vault secret store authentication methods (#4110)
Support for newer Databricks versions (#4144)

Fixed

Port reuse for local deployments
Parallel deployment invocations
Keyboard interrupt handling during monitoring
Case-sensitivity issues when updating entity names (#4140)

View full release on GitHub

0.91.0 (2025-10-25)

See what's new and improved in version 0.91.0.

Local Deployer

Deploy pipelines locally with full control
Perfect for development and testing workflows

Learn more | PR #4085

Advanced Caching System

File and object-based cache invalidation
Cache expiration for bounded lifetime
Custom cache functions for advanced logic

Read the documentation | PR #4040

Deployment Visualizations

Attach custom visualizations to deployments
Fully customizable deployment server settings
Enhanced deployment management

PR #4016 | PR #4064

Python 3.13 Support

Full compatibility with Python 3.13
MLX array materializer for Apple Silicon

PR #4053 | PR #4027

UI Updates

Deployment Playground: Easier to invoke and test deployments (#861)
Global Lists: Centralized access for deployments (#851) and snapshots (#854)
Create Snapshots: Create snapshots directly from the UI (#856)
GitHub-Flavored Markdown support (#876)
Resizable Panels (#873)

Improved

Customizable image tags for Docker builds (#4025)
Enhanced deployment server configuration (#4064)
Better integration with MLX arrays (#4027)

Fixed

Print capturing incompatibility with numba (#4060)
Hashicorp Vault secrets store mount point configuration (#4088)

Breaking Changes

Dropped Python 3.9 support - upgrade to Python 3.10+ (#4053)

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0.90.0 (2025-10-02)

See what's new and improved in version 0.90.0.

Pipeline Snapshots & Deployments

Capture immutable snapshots of pipeline code and configuration
Deploy pipelines as HTTP endpoints for online inference
Docker, AWS, and GCP deployer implementations

Learn more about Snapshots | Learn more about Deployments

PR #3856 | PR #3920

Runtime Environment Variables

Configure environment variables when running pipelines
Support for ZenML secrets in runtime configuration

PR #3336

Dependency Management Improvements

Reduced base package dependencies
Local database dependencies moved to zenml[local] extra
JAX array materializer support

PR #3916 | PR #3712

UI Updates

Pipeline Snapshots & Deployments: Track entities introduced in ZenML 0.90.0 (#814)

Improved

Slimmer base package for faster installations (#3916)
Better dependency management
Enhanced JAX integration (#3712)

Breaking Changes

Client-Server compatibility: Must upgrade both simultaneously
Run templates need to be recreated
Base package no longer includes local database dependencies - install zenml[local] if needed (#3916)

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