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0.13.2

Not secure
* You can now run your pipelines locally in isolated Docker containers per step
* gabrielmbmb updated our MLFlow experiment tracker to work with Databricks deployments 🎉
* Documentation updates for cloud deployments and multi-tenancy Kubeflow support

What's Changed
* Update GitHub Actions by fa9r in https://github.com/zenml-io/zenml/pull/864
* Raise zenml exception when cyclic graph is detected by schustmi in https://github.com/zenml-io/zenml/pull/866
* Add source to segment identify call by htahir1 in https://github.com/zenml-io/zenml/pull/868
* Use default local paths/URIs for the local artifact and metadata stores by stefannica in https://github.com/zenml-io/zenml/pull/873
* Implement local docker orchestrator by schustmi in https://github.com/zenml-io/zenml/pull/862
* Update cheat sheet with latest CLI commands from 0.13.0 by dnth in https://github.com/zenml-io/zenml/pull/867
* Add a note about importing proper DockerConfiguration module by jsuchome in https://github.com/zenml-io/zenml/pull/877
* Bugfix/misc by schustmi in https://github.com/zenml-io/zenml/pull/878
* Fixed bug in tfx by htahir1 in https://github.com/zenml-io/zenml/pull/883
* Mlflow Databricks connection by gabrielmbmb in https://github.com/zenml-io/zenml/pull/882
* Refactor cloud guide to stack deployment guide by wjayesh in https://github.com/zenml-io/zenml/pull/861
* Add cookie consent by strickvl in https://github.com/zenml-io/zenml/pull/871
* Stack recipe CLI improvements by wjayesh in https://github.com/zenml-io/zenml/pull/872
* Kubeflow workaround added by htahir1 in https://github.com/zenml-io/zenml/pull/886


**Full Changelog**: https://github.com/zenml-io/zenml/compare/0.13.1...0.13.2

0.13.1

Not secure
* You can now specify the exact order in which your pipelines steps should be
executed, e.g., via `step_b.after(step_a)`
* TensorBoard was moved to a separate integration so you can use it with Pytorch
and other modeling frameworks
* You can now configure the Evidently integration to ignore specific columns in
your datasets.

This release also contains a lot of documentation on how to deploy
custom code (like preprocessing and postprocessing code) with our KServe and
Seldon integrations.

What's Changed
* Fix flag info on recipes in docs by wjayesh in https://github.com/zenml-io/zenml/pull/854
* Fix some materializer issues by schustmi in https://github.com/zenml-io/zenml/pull/852
* Add ignore columns for evidently drift detection by SangamSwadiK in https://github.com/zenml-io/zenml/pull/851
* TensorBoard Integration by fa9r in https://github.com/zenml-io/zenml/pull/850
* Add option to specify task dependencies by schustmi in https://github.com/zenml-io/zenml/pull/858
* Custom code readme and docs by safoinme in https://github.com/zenml-io/zenml/pull/853

New Contributors
* SangamSwadiK made their first contribution in https://github.com/zenml-io/zenml/pull/851

**Full Changelog**: https://github.com/zenml-io/zenml/compare/0.13.0...0.13.1

0.13.0

Not secure
<img src="https://user-images.githubusercontent.com/6821286/185903222-12dfbc85-b260-42ae-aae8-dd2ee498971a.gif" width="400" height="400" />

ZenML version 0.13.0 is chock-full with exciting features.

[Custom Code Deployment](https://github.com/zenml-io/zenml/tree/main/examples/custom_code_deployment) is the
continuation of the Model Deployment story that we have been working on over the last few releases. Now
it is possible to deploy custom code along with your models using Kserve or Seldon.

With [Spark](https://github.com/zenml-io/zenml/tree/main/examples/spark_distributed_programming) this release also brings distributed processing into the ZenML toolkit.

Spinning up and configuring infrastructure is a difficult part of the MLOps journey
and can easily become a barrier to entry. Using our [mlops-stacks](https://github.com/zenml-io/mlops-stacks)
repository, it is now possible to spin up perfectly configured infrastructure with
the corresponding ZenML stack using the ZenML CLI.

As always, we've also included various bug fixes and lots of improvements to the documentation and our examples.

Breaking Changes

This release introduces a breaking change to the CLI by adjusting the access to
the stack component specific resources for `secret-managers` and
`model-deployers` to be more explicitly linked to the component. Here is how:

bash
`zenml secret register ...` becomes
zenml secrets-manager secret register ...

`zenml served_models list` becomes
zenml model-deployer models list


What's Changed
* Link checker by dnth in https://github.com/zenml-io/zenml/pull/818
* Update Readme with latest info from docs page by dnth in https://github.com/zenml-io/zenml/pull/810
* Typo on Readme by dnth in https://github.com/zenml-io/zenml/pull/821
* Update kserve installation to 0.9 on kserve deployment example by safoinme in https://github.com/zenml-io/zenml/pull/823
* Allow setting caching via the `config.yaml` by strickvl in https://github.com/zenml-io/zenml/pull/827
* Handle file-io with context manager by aliabbasjaffri in https://github.com/zenml-io/zenml/pull/825
* Add automated link check github actions by dnth in https://github.com/zenml-io/zenml/pull/828
* Fix the SQL zenstore to work with MySQL by stefannica in https://github.com/zenml-io/zenml/pull/829
* Improve label studio error messages if secrets are missing or of wrong schema by schustmi in https://github.com/zenml-io/zenml/pull/832
* Add secret scoping to the Azure Key Vault by stefannica in https://github.com/zenml-io/zenml/pull/830
* Unify CLI concepts (removing `secret`, `feature` and `served-models`) by strickvl in https://github.com/zenml-io/zenml/pull/833
* Put link checker as part of CI by dnth in https://github.com/zenml-io/zenml/pull/838
* Add missing requirement for step operators by schustmi in https://github.com/zenml-io/zenml/pull/834
* Fix broken links from link checker results by dnth in https://github.com/zenml-io/zenml/pull/835
* Fix served models logs formatting error by safoinme in https://github.com/zenml-io/zenml/pull/836
* New Docker build configuration by schustmi in https://github.com/zenml-io/zenml/pull/811
* Secrets references on stack component attributes by schustmi in https://github.com/zenml-io/zenml/pull/817
* Misc bugfixes by schustmi in https://github.com/zenml-io/zenml/pull/842
* Pillow Image materializer by strickvl in https://github.com/zenml-io/zenml/pull/820
* Add Tekton orchestrator by schustmi in https://github.com/zenml-io/zenml/pull/844
* Put Slack call to action at the top of README page. by dnth in https://github.com/zenml-io/zenml/pull/846
* Change Quickstart to Use Tabular Data by fa9r in https://github.com/zenml-io/zenml/pull/843
* Add sleep before docker builds in release GH action by schustmi in https://github.com/zenml-io/zenml/pull/849
* Implement Recursive Built-In Container Materializer by fa9r in https://github.com/zenml-io/zenml/pull/812
* Custom deployment with KServe and Seldon Core by safoinme in https://github.com/zenml-io/zenml/pull/841
* Spark Integration by bcdurak in https://github.com/zenml-io/zenml/pull/837
* Add zenml stack recipe CLI commands by wjayesh in https://github.com/zenml-io/zenml/pull/807

New Contributors
* aliabbasjaffri made their first contribution in https://github.com/zenml-io/zenml/pull/825

**Full Changelog**: https://github.com/zenml-io/zenml/compare/0.12.0...0.13.0

0.12.0

Not secure
<img src="https://user-images.githubusercontent.com/3963946/182412235-0fa429fb-295f-4634-bcc5-ccbdfe893afc.gif" width="400" height="400" />

The 0.12.0 release comes with the third implementation of the ZenML Model Deployer abstraction: The [KServe](https://github.com/zenml-io/zenml/tree/main/examples/kserve_deployment) integration allows you to deploy any PyTorch, TensorFlow or SKLearn from within your ZenML pipelines!

We also added functionality to specify hardware resources on a step level to control the amount of memory, CPUs and GPUs that each ZenML step has access to. This is curretly limited to the Kubeflow and Vertex orchestrator but will be expanded in upcoming releases.

Additionally, we've added support for scoped secrets in our AWS, GCP and Vault Secrets Managers. These updated Secrets Managers allow you to configure a scope which determines if secrets are shared with other ZenML Secrets Managers using the same backend.

As always, we've also included various bug fixes and lots of improvements to the documentation and our examples.

What's Changed

- Fix Links on the examples by safoinme in https://github.com/zenml-io/zenml/pull/782
- Fix broken links in source code by schustmi in https://github.com/zenml-io/zenml/pull/784
- Invalidating artifact/metadata store if there is a change in one of them by bcdurak in https://github.com/zenml-io/zenml/pull/719
- Fixed broken link in README by htahir1 in https://github.com/zenml-io/zenml/pull/785
- Embed Cheat Sheet in a separate docs page by fa9r in https://github.com/zenml-io/zenml/pull/790
- Add data validation documentation by stefannica in https://github.com/zenml-io/zenml/pull/789
- Add local path for mlflow experiment tracker by schustmi in https://github.com/zenml-io/zenml/pull/786
- Improve Docker build logs. by fa9r in https://github.com/zenml-io/zenml/pull/793
- Allow standard library types in steps by stefannica in https://github.com/zenml-io/zenml/pull/799
- Added small description by AlexejPenner in https://github.com/zenml-io/zenml/pull/801
- Replace the restriction to use Repository inside step with a warning by stefannica in https://github.com/zenml-io/zenml/pull/792
- Adjust quickstart to data validators by fa9r in https://github.com/zenml-io/zenml/pull/797
- Add utility function to deprecate pydantic attributes by schustmi in https://github.com/zenml-io/zenml/pull/778
- Fix the mismatch KFP version between Kubeflow and GCP integration by safoinme in https://github.com/zenml-io/zenml/pull/796
- Made mlflow more verbose by htahir1 in https://github.com/zenml-io/zenml/pull/802
- Fix links by dnth in https://github.com/zenml-io/zenml/pull/798
- KServe model deployer integration by stefannica in https://github.com/zenml-io/zenml/pull/655
- retrieve pipeline requirement within running step by safoinme in https://github.com/zenml-io/zenml/pull/805
- Fix --decouple_stores error message by strickvl in https://github.com/zenml-io/zenml/pull/814
- Support subscripted generic step output types by fa9r in https://github.com/zenml-io/zenml/pull/806
- Allow empty kubeconfig when using local kubeflow orchestrator by schustmi in https://github.com/zenml-io/zenml/pull/809
- fix the secret register command in kserve docs page by safoinme in https://github.com/zenml-io/zenml/pull/815
- Annotation example (+ stack component update) by strickvl in https://github.com/zenml-io/zenml/pull/813
- Per-step resource configuration by schustmi in https://github.com/zenml-io/zenml/pull/794
- Scoped secrets by stefannica in https://github.com/zenml-io/zenml/pull/803
- Adjust examples and docs to new pipeline and step fetching syntax by fa9r in https://github.com/zenml-io/zenml/pull/795

Full Changelog: https://github.com/zenml-io/zenml/compare/0.11.0...0.12.0

0.11.0

Not secure
Our 0.11.0 release contains our new annotation workflow and stack component. We've been blogging [about](https://blog.zenml.io/open-source-data-annotation-tools/) this for a few weeks, and even started maintaining [our own repository](https://github.com/zenml-io/awesome-open-data-annotation) of open-source annotation tools. With ZenML 0.11.0 you can bring data labeling into your MLOps pipelines and workflows as a first-class citizen. We've started our first iteration of this functionality by integrating with [Label Studio](https://labelstud.io/), a leader in the open-source annotation tool space.

This release also includes a ton of updates to our documentation. (Seriously, go check them out! We added tens of thousands of words since the last release.) We continued the work on our data validation story from the previous release: [Deepchecks](https://deepchecks.com/) is the newest data validator we support, and we updated our Evidently and whylogs integrations to include all the latest and greatest from those tools.

Beyond this, as usual we included a number of smaller bugfixes and documentation changes to cumulatively improve experience of using ZenML as a user. For a detailed look at what's changed, give [our full release notes](https://github.com/zenml-io/zenml/releases/tag/0.11.0) a glance.

🏷 Data Annotation with Label Studio

We've been hard at work on our new stack component and integration with [Label Studio](https://labelstud.io/), the first of our data annotation tools. 🥳

![Object Detection with ZenML and Label Studio](https://user-images.githubusercontent.com/3348134/179927899-b7ab633c-814f-4b76-b53f-a3504ec3814b.png)

Annotators are a stack component that enables the use of data annotation as part of your ZenML stack and pipelines. You can use the associated CLI command to launch annotation, configure your datasets and get stats on how many labeled tasks you have ready for use.

Data annotation/labeling is a core part of MLOps that is frequently left out of the conversation. With this release, ZenML now supports annotation as an integrated and first-class citizen as part of the MLOps lifecycle.

Got feedback or just want to let us know how you feel? [Connect with us](https://zenml.io/slack-invite/) or join us for a [Community Meetup](https://www.eventbrite.de/e/zenml-meet-the-community-tickets-354426688767) 👋 which happens every Wednesday!

🔎 More Data Validation with Deepchecks, Evidently & whylogs

We continued the work started in the previous release, adding a new integration with [Deepchecks](https://deepchecks.com/). Deepchecks is a feature-rich data validation open-source library to painlessly do data validation. Deepchecks can do a variety of data validation tasks, from data integrity checks that work with a single dataset to data+model evaluation to data drift analyses. All this can be done with minimal configuration input from the user, or customized with specialized conditions that the validation checks should perform. Check out [our example](https://github.com/zenml-io/zenml/tree/main/examples/deepchecks_data_validation) if you want to see it in action!

We also updated our integrations with Great Expectations, Evidently and whylogs to support their latest releases. These tools have added lots of great new features in recent weeks and we now support most of them, all under the new data validator stack component and abstraction.

📖 Documentation & User Guides

We made a significant overhaul of our documentation since the last release:

- The developer guide section is reworked to be more complete and beginner-friendly
- We wrote a whole new 'MLOps stack' section, which contains detailed explanations for all MLOps stack components and their various implementations in ZenML
- A new 'Cloud Guide' section contains complete setup guides for multiple cloud stacks. This will help you get started quickly.
- We added [a new ZenML cheatsheet](https://storage.googleapis.com/zenml-public-bucket/zenml_cheat_sheet.pdf) that you can use to remind you of important CLI commands.

⌨️ CLI Improvements

We fixed a bug that was preventing users who upgraded to 0.10.0 from pulling new examples. This now works without any problem.

➕ Other Updates, Additions and Fixes

The latest release includes several smaller features and updates to existing functionality:

- We fixed a bug in our Feast integration that prevented registration as a stack component.
- We updated the structure of all our examples so that they now conform to all of [the 'best practices' guidance](https://docs.zenml.io/v/docs/resources/best-practices) we've made available in our docs.
- We fixed some module and path resolution errors that were happening for Windows users.
- We have combined all the MetadataStore contexts to speed up calls to the metadata store. This speeds up pipeline execution.
- We now prevent providing extra attributes when initializing stack components. This could have led to unexpected behaviors so we now just prevent this behavior.
- We've built several new Docker images. You can view them all over at [dockerhub](https://hub.docker.com/r/zenmldocker/zenml/tags).
- The facets magic display now works on Google Colab.
- Our Azure Secret Schema now works with the secrets manager. An issue with how Azure handles secret names was preventing this, but we encoded the secret names to get around this shortcoming on the Azure platform.
- Val3nt-ML added a nested MLflow parameter (on the `enable_mlflow` decorator) which will allow the creation of nested runs for each step of a ZenML pipeline in MLflow.
- We enabled the fetching of secrets from within a step.
- We now allow the fetching of pipelines and steps by name, class or instance.
- You can now also add optional machine specs to VertexAI orchestrators, thanks to a PR from felixthebeard.
- We fixed a bug that was preventing users from importing pipeline requirements via a `requirements.txt` file if the file ended with a newline.

Breaking Changes

The 0.11.0 release remodels the Evidently and whylogs integrations as Data Validator stack components, in an effort to converge all data profiling and validation libraries around the same abstraction. As a consequence, you now need to configure and add a Data Validator stack component to your stack if you wish to use Evidently or whylogs in your pipelines:

* for Evidently:

shell
zenml data-validator register evidently -f evidently
zenml stack update -dv evidently


* for whylogs:

shell
zenml data-validator register whylogs -f whylogs
zenml stack update -dv whylogs


In this release, we have also upgraded the Evidently and whylogs libraries to their latest and greatest versions (whylogs 1.0.6 and evidently 0.1.52). These versions introduce non-backwards compatible changes that are also reflected in the ZenML integrations:

* Evidently profiles are now materialized using their original `evidently.model_profile.Profile ` data type and the builtin `EvidentlyProfileStep` step now also returns a `Profile` instance instead of the previous dictionary representation. This may impact your existing pipelines as you may have to update your steps to take in `Profile` artifact instances instead of dictionaries.

* the whylogs `whylogs.DatasetProfile` data type was replaced by `whylogs.core.DatasetProfileView` in the builtin whylogs materializer and steps. This may impact your existing pipelines as you may have to update your steps to return and take in `whylogs.core.DatasetProfileView` artifact instances instead of `whylogs.DatasetProfile` objects.

* the whylogs library has gone through a major transformation that completely removed the session concept. As a result, the `enable_whylogs` step decorator was replaced by an `enable_whylabs` step decorator. You only need to use the step decorator if you wish to log your profiles to the Whylabs platform.

Please refer to the examples provided for Evidently and whylogs to learn more about how to use the new integration versions:

* [Evidently](https://github.com/zenml-io/zenml/tree/main/examples/evidently_drift_detection)
* [whylogs/Whylabs](https://github.com/zenml-io/zenml/tree/main/examples/whylogs_data_profiling)

🙌 Community Contributions

We received several new community contributions during this release cycle. Here's everybody who contributed towards this release:

* [jsuchome](https://github.com/jsuchome) made their first contribution in https://github.com/zenml-io/zenml/pull/740
* [Val3nt-ML](https://github.com/Val3nt-ML) made their first contribution in https://github.com/zenml-io/zenml/pull/742
* [felixthebeard](https://github.com/felixthebeard) contributed a PR to allow
for optional machine specs to be passed in for the VertexAI orchestrator in https://github.com/zenml-io/zenml/pull/762

👩‍💻 Contribute to ZenML!

Join our [Slack](https://zenml.io/slack-invite/) to let us know if you have an
idea for a feature or something you'd like to contribute to the framework.

We have a [new home for our roadmap](https://zenml.io/roadmap) where you can vote on your favorite upcoming
feature or propose new ideas for what the core team should work on. You can vote
without needing to log in, so please do let us know what you want us to build!

0.10.0

Not secure
<img src="https://user-images.githubusercontent.com/4792082/176142306-22400203-a2d9-498c-aff9-0f807f05c2c3.gif" data-canonical-src="https://user-images.githubusercontent.com/4792082/176142306-22400203-a2d9-498c-aff9-0f807f05c2c3.gif" width="960" height="540" />

The 0.10.0 release continues our streak of extending ZenML with support for new orchestrators, this time by adding [the Kubernetes Native Orchestrator](https://github.com/zenml-io/zenml/tree/main/examples/kubernetes_orchestration). This orchestrator is a lightweight alternative to other distributed orchestrators like Airflow or Kubeflow that gives our users the ability to run pipelines in any Kubernetes cluster without having to install and manage additional tools or components.

This release features another integration that we are really excited about: the popular data profiling and validation library [Great Expectations](https://greatexpectations.io/) is our first Data Validator, a new category of stack components that we are in the process of standardizing, that will make data quality a central feature of ZenML. [The ZenML Great Expectations integration](https://github.com/zenml-io/zenml/tree/main/examples/great_expectations_data_validation) eliminates the complexity associated with configuring the store backends for Great Expectations by reusing our Artifact Store concept for that purpose and gives ZenML users immediate access to Great Expectations in both local and cloud settings.

Last but not least, the release also includes a new secrets manager implementation, courtesy of our contributor karimhabush, that integrates ZenML with the [Hashicorp Vault Server](https://www.vaultproject.io) as well as a few other bug fixes and improvements.

What's Changed
* Fix broken link by strickvl in https://github.com/zenml-io/zenml/pull/707
* Add stack component copy command by schustmi in https://github.com/zenml-io/zenml/pull/705
* Remove `force` flag from secrets managers' implementation by strickvl in https://github.com/zenml-io/zenml/pull/708
* Fixed wrong example README by AlexejPenner in https://github.com/zenml-io/zenml/pull/712
* Fix dead links in integrations docs. by fa9r in https://github.com/zenml-io/zenml/pull/710
* Fixing link to guide by chethanuk-plutoflume in https://github.com/zenml-io/zenml/pull/716
* Adding azure-keyvault-secrets to azure integration dependencies by safoinme in https://github.com/zenml-io/zenml/pull/717
* Fix MLflow repeated deployment error by fa9r in https://github.com/zenml-io/zenml/pull/715
* Replace alerter standard steps by Slack-specific steps to fix config issue. by fa9r in https://github.com/zenml-io/zenml/pull/714
* Fix broken links on README by dnth in https://github.com/zenml-io/zenml/pull/722
* Invalidate cache by strickvl in https://github.com/zenml-io/zenml/pull/724
* Skip Cleaning Trace on tests by safoinme in https://github.com/zenml-io/zenml/pull/725
* Kubernetes orchestrator by fa9r in https://github.com/zenml-io/zenml/pull/688
* Vault Secrets Manager integration - KV Secrets Engine by karimhabush in https://github.com/zenml-io/zenml/pull/689
* Add missing help text for CLI commands by safoinme in https://github.com/zenml-io/zenml/pull/723
* Misc bugfixes by schustmi in https://github.com/zenml-io/zenml/pull/713
* Great Expectations integration for data validation by strickvl in https://github.com/zenml-io/zenml/pull/555
* Fix GCP artifact store by schustmi in https://github.com/zenml-io/zenml/pull/730

New Contributors
* chethanuk-plutoflume made their first contribution in https://github.com/zenml-io/zenml/pull/716
* dnth made their first contribution in https://github.com/zenml-io/zenml/pull/722
* karimhabush made their first contribution in https://github.com/zenml-io/zenml/pull/689

**Full Changelog**: https://github.com/zenml-io/zenml/compare/0.9.0...0.10.0

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