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+---
+title: "Kubeflow User Survey 2023"
+layout: post
+toc: false
+comments: true
+image: images/logo.png
+hide: false
+categories: [community]
+permalink: /kubeflow-user-survey-2023/
+author: "Anna Jung, Johnu George, and Josh Bottum"
+---
+
+In April 2023, the Kubeflow user survey opened, gathering community feedback. The survey aimed to comprehend the
+adoption of Kubeflow and collect input on the benefits, gaps, and requirements for machine learning use cases.
+
+The survey consisted 21 questions, including multiple-choice and freeform formats. It ran from April 11th to May 26th
+and received 90 responses. The 2023 survey yielded more targeted and actionable freeform answers, which provided further
+insights into users’ requirements and potential enhancements for Kubeflow. Furthermore, we received a significant amount
+of positive feedback regarding the factors that contribute to the success of Kubeflow and its community. One survey
+respondent expressed their appreciation, stating, *“I love that it helps teams do high-quality ML work while providing
+flexibility. I love that it is open-source. I love that the community keeps working hard and making it better.”* Another
+respondent highlighted their liking for Kubeflow due to *“the broad potential of some of the projects (like Pipelines
+components) and the different integrations that make for more of an end-to-end experience.”*
+
+We thank everyone for participating in the survey and we will use both the improvements and positive feedback to steer
+our efforts in enhancing Kubeflow, ensuring it remains a user-centric platform for all.
+
+## Key Takeaways
+
+- 84% of the users deploy more than one Kubeflow component
+- The top 3 Kubeflow components are Pipelines (90%), Notebooks (76%), and Katib (47%)
+- The top contrib component used by the user is KServe (62%)
+- Documentation (55%) is the biggest gap in Kubeflow, followed by tutorials (50%) and tied for third place,
+  installation (39%) and upgrades (39%)
+- Monitoring models (45%) is the biggest gap in the users' ML lifecycle, followed by model registry (44%) and initial
+  setup (39%)
+- 52% of the users use the raw manifest installation to install Kubeflow
+- The top distribution used to install Kubeflow is AWS (28%) followed by Google Cloud (17%)
+- 74% of the users deploy Kubeflow on the cloud and 45% on-prem
+- 49% of the users are running Kubeflow in production
+- 17% of the users are contributing back to Kubeflow
+- 49% of the users are keeping up with the latest Kubeflow 1.7 release and 43% are running a version behind Kubeflow 1.6
+
+### Survey Respondents
+
+![2023 survey demographics and location graph](/images/2023-07-26-kubeflow-user-survey-2023/demographics_location_industry.png)
+
+The Kubeflow user survey drew responses from 90 members of the community mostly made up of members from the United
+States (43%), Europe (34%), and Asia-Pacific (10%). The majority of the respondents were from the Tech industry (49%),
+followed by Finance (13%) and Consulting (11%).
+
+While there is diversity in roles that make up the community, most of the community members had the title MLOps
+Engineer (18%), ML Engineer (17%), and Architect (15%).
+
+In the last year’s 2022 User Survey, we started to see an increase in the number of users adopting Kubeflow in the
+production environment and actively contributing to the Kubeflow project as their expertise with the project grew. This
+year, we see the trend continuing with the highest percentage of Kubeflow running in production (49%) and the highest
+number of users contributing to the project (17%). As the adoption of Kubeflow continues to rise, this will have a
+significant impact on the positive growth and development of the project.
+
+![2023 survey demographics kubeflow experience graph](/images/2023-07-26-kubeflow-user-survey-2023/demographics_kubeflow_experience.png)
+
+### Documentation and Tutorials
+
+![2023 survey Kubeflow gaps graph](/images/2023-07-26-kubeflow-user-survey-2023/kubeflow_gaps.png)
+
+Documentation and tutorial have been a challenge in the community for a while, and the trend continues this year with
+documentation (55%) voted as the number one biggest gap in Kubeflow, followed by Tutorials (49%) as the second biggest
+gap.
+
+In the previous surveys, the feedback on documentation and tutorials was very generic. However, this year, the users are
+asking for specific documentation to help them adopt Kubeflow better. Some of the documentation and tutorials users are
+looking for are the following:
+
+- Tutorials and examples of how to leverage the new features delivered with the new releases
+- An architecture diagram of Kubeflow of how each component works
+- How to setup Transport Layer Security (TLS) with raw manifest installation
+- A user onboarding documentation on how to add contributors to the platform and map credentials
+- Documentation to show integration with other tools like MLflow
+- Examples of all the capabilities of KFP packages
+- Documentation to cover the RBAC and authorization in Kubeflow
+- Upgrade guide for every release
+
+In addition to the specific ask, the major themes in what type of documentation improvements users are asking for are
+
+- Documentation for advanced users and advanced use cases
+- Documentation for best practices around Kubeflow
+- More diverse end-to-end examples, not just the mnist example
+- Frequent updates to documentation as many are out of date
+
+To address the challenges of Kubeflow documentation, we are looking for help from our community. If you are interested
+in improving Kubeflow documentation and forming a Kubeflow Documentation Team, join us at the next community meeting to
+introduce yourself to the community.
+
+### Installation and Upgrades
+
+![2023 survey Kubeflow installation graph](/images/2023-07-26-kubeflow-user-survey-2023/kubeflow_installation.png)
+
+One of the top three voted answers to the biggest gap in Kubeflow was installation tied with upgrades. The survey result
+showed that 52% of the users use the raw manifest installation to install Kubeflow. With the sole support of Kustomize
+as a choice of installation tool for Kubeflow, many users are looking for more diverse support for tooling, especially
+Helm.
+
+One of the biggest reasons for Helm support requests is due to complexity of installation. One survey respondent shared,
+*“Kustomize does not quite provide the same experience and requires a lot more familiarity with the underlying systems
+and manifests to properly configure…,”* which makes installation too difficult for a small team trying to adopt
+Kubeflow.
+
+For some, support for a different installation tool isn’t the answer. We also received much feedback on the installation
+needing to be more lightweight. Due to the complexity of the stack and integration of many components, people are
+struggling to customize their kubeflow instance due to tight coupling and requirements of large resources.
+
+In addition to installation, upgrades are also on top of the mind for many of the users. While there are some upgrade
+guides for specific distributions, many of the users are installing Kubeflow without distribution. Without upgrade
+guides provided for the raw manifest installation, upgrades take a great amount of effort for the users without
+guaranteeing that upgrading Kubeflow will not introduce breaking changes to their current setup. One survey respondent
+expressed, *“It's currently not possible to guarantee that upgrading kubeflow versions will work. We need to delete the
+entire cluster, database, and all the resources just to re-install from scratch. This generally results in losing data (
+because nothing guarantees that the DB scheme will be compatible from one version to another). There should be an easy
+way to upgrade kubeflow.”*
+
+### Monitoring Models and Model Registry
+
+![2023 survey Kubeflow ML lifecycle gaps graph](/images/2023-07-26-kubeflow-user-survey-2023/kubeflow_gaps_ml_lifecycle.png)
+
+In last year’s survey, monitoring surged to the top as the biggest gap in the machine learning lifecycle. This year, the
+trend continued with monitoring (45%) voted as the number one concern, closely followed by model registry (44%).
+
+For both gaps, users are looking for a built-in solution that addresses monitoring and model registry needs, with
+specific requests for support of Grafana and MLflow integrations to enable a seamless experience with Kubeflow
+pipelines. With a lack of support for both of these tools, users are expressing concerns about the challenges they face
+in managing and tracking their models.
+
+### Security
+
+In the previous [2022 Kubeflow User Survey](https://blog.kubeflow.org/kubeflow-user-survey-2022/), Security was one of
+the top three voted answers as the biggest gap in Kubeflow. Since then, security has been on top of the mind for the
+community, forming an unofficial security team with secutity focused minded members to
+
+In the previous 2022 Kubeflow User Survey, security emerged as one of the top three voted answers, highlighting it as a
+significant gap in Kubeflow. Since then, the community has placed a strong emphasis on security, leading to the
+formation of an unofficial security team comprising members with a security-focused mindset. Their primary objective is
+to address security concerns and ensure that Kubeflow remains a robust and secure platform for all its users. For
+further details, visit the [Kubeflow security team](https://github.com/kubeflow/kubeflow/tree/master/security) and
+consider joining the next security team meeting.
+
+## What’s Next?
+
+Currently, all working groups are working towards the 1.8 release, planned to release
+on [October 4th, 2023](https://github.com/kubeflow/community/tree/master/releases/release-1.8). Upon successful delivery
+of the 1.8 release, each working group will shift their focus towards prioritizing features for the next subsequent
+release. During the initial planning phase of the new release, each working group leads will reevaluate the survey
+results to determine their priorities driven by your survey feedback. If you are interested in the future direction of
+each working group, join them in discussing their roadmap at their future meetings. All Kubeflow community meetings can
+be found in
+the [Kubeflow community calendar](https://www.kubeflow.org/docs/about/community/#kubeflow-community-calendars).
+
+As for improvements to the documentation and tutorials, Kubeflow community is actively seeking help from our community
+members. If you are interested in improving Kubeflow documentation and forming a Kubeflow Documentation Team to tackle
+current challenges and work on future improvements, we invite you to join us at the next community meeting to introduce
+yourself to the community and express your interest in joining this effort.
+
+## Join the Community
+
+We would like to thank everyone for their participation in the survey. As you can see from the survey results, the
+Kubeflow Community is vibrant and diverse, solving real-world problems for organizations worldwide.
+
+Want to help? The Kubeflow Community Working Groups hold open meetings and are always looking for more volunteers and
+users to unlock the potential of machine learning. If you’re interested in becoming a Kubeflow contributor, please feel
+free to check out the resources below. We look forward to working with you!
+
+- Visit our [Kubeflow website](https://www.kubeflow.org/) or [Kubeflow GitHub Page](https://github.com/kubeflow)
+- Join
+  the [Kubeflow Slack](https://join.slack.com/t/kubeflow/shared_invite/enQtMjgyMzMxNDgyMTQ5LWUwMTIxNmZlZTk2NGU0MmFiNDE4YWJiMzFiOGNkZGZjZmRlNTExNmUwMmQ2NzMwYzk5YzQxOWQyODBlZGY2OTg)
+  workspace
+- Join the [kubeflow-discuss](https://groups.google.com/g/kubeflow-discuss) mailing list
+- Attend a [weekly community meeting](https://www.kubeflow.org/docs/about/community/)
+
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