<commit_before>---
title: "Kubeflow Continues to Move into Production"
description: "2021 State of the Kubeflow World"
layout: post
toc: true
comments: true
image: images/logo.png
hide: false
categories: [release]
permalink: /kubeflow-continues-to-move-to-production
author: "\"<a href='https://www.linkedin.com/in/joshbottum/'><NAME></a>\", <a href=\"https://twitter.com/aronchick\"><NAME></a>, <a href=\"https://www.linkedin.com/in/thealamkin\"><NAME></a>\n"
---

Kubeflow Users are maturing and the community is growing, forty eight percent of users are supporting deployments in production.

The Spring 2021 [Kubeflow](http://kubeflow.org/) Community User Survey collected input from Kubeflow users on the benefits, gaps and requirements for machine learning use cases. It is the largest survey to date with 179 responses—a 50% increase from the [Kubeflow 1.0 Community User Survey](https://medium.com/kubeflow/kubeflow-community-user-survey-fall-2019-a84776c71743) a year ago. The Survey respondents span a spectrum of skill sets. While 42% are machine learning (ML) engineers, and 24% are ML architects, the titles of the respondents vary from DevOps Engineers to data scientists and product managers.

![alt_text](images/2021-03-11-survey/image1.png)

# Kubeflow Continues to Move Into Production

Forty eight percent of users are supporting deployments that are in production, up from 15% last year. Further, one question that many folks have is “do people upgrade a production deployment or just install a new cluster and start over?” It appears that the latter is far more common: just 8% have upgraded their environment.

![alt_text](images/2021-03-11-survey/image2.png)

Similar to previous years, Kubeflow Pipelines and Notebooks are the most popular components, but other components are now being widely deployed as well. Interest in TensorBoard has grown, joining KFServing, Katib (AutoML), and Distributed Training as top additional services.

![alt_text](images/2021-03-11-survey/image3.png)

Although the usage patterns for Kubeflow components are mixed, the vast majority of users need at least two Kubeflow components in their ML Platform.  

![alt_text](images/2021-03-11-survey/image4.png)

TensorFlow is the leading ML Framework, followed by Scikit-learn, PyTorch, Keras, and XGBoost. However, with Kubeflow’s built-in extensibility, the type of ML tools people use in Kubeflow go beyond just training frameworks, and include MLFlow, Airflow, and Spark.

![alt_text](images/2021-03-11-survey/image5.png)

# VS Code and Jupyter Lead Dev Environments<commit_msg>Fix links for authors (#80)<commit_after>---
title: "Kubeflow Continues to Move into Production"
description: "2021 State of the Kubeflow World"
layout: post
toc: true
comments: true
image: images/logo.png
hide: false
categories: [release]
permalink: /kubeflow-continues-to-move-to-production
author: "<NAME>, <NAME>, <NAME>"
---

Kubeflow Users are maturing and the community is growing, forty eight percent of users are supporting deployments in production.

The Spring 2021 [Kubeflow](http://kubeflow.org/) Community User Survey collected input from Kubeflow users on the benefits, gaps and requirements for machine learning use cases. It is the largest survey to date with 179 responses—a 50% increase from the [Kubeflow 1.0 Community User Survey](https://medium.com/kubeflow/kubeflow-community-user-survey-fall-2019-a84776c71743) a year ago. The Survey respondents span a spectrum of skill sets. While 42% are machine learning (ML) engineers, and 24% are ML architects, the titles of the respondents vary from DevOps Engineers to data scientists and product managers.

![alt_text](images/2021-03-11-survey/image1.png)

# Kubeflow Continues to Move Into Production

Forty eight percent of users are supporting deployments that are in production, up from 15% last year. Further, one question that many folks have is “do people upgrade a production deployment or just install a new cluster and start over?” It appears that the latter is far more common: just 8% have upgraded their environment.

![alt_text](images/2021-03-11-survey/image2.png)

Similar to previous years, Kubeflow Pipelines and Notebooks are the most popular components, but other components are now being widely deployed as well. Interest in TensorBoard has grown, joining KFServing, Katib (AutoML), and Distributed Training as top additional services.

![alt_text](images/2021-03-11-survey/image3.png)

Although the usage patterns for Kubeflow components are mixed, the vast majority of users need at least two Kubeflow components in their ML Platform.  

![alt_text](images/2021-03-11-survey/image4.png)

TensorFlow is the leading ML Framework, followed by Scikit-learn, PyTorch, Keras, and XGBoost. However, with Kubeflow’s built-in extensibility, the type of ML tools people use in Kubeflow go beyond just training frameworks, and include MLFlow, Airflow, and Spark.

![alt_text](images/2021-03-11-survey/image5.png)

# VS Code and Jupyter Lead Dev Environments
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