diff --git a/mlflow/2.11.1/22.03-lts-sp3/Dockerfile b/mlflow/2.11.1/22.03-lts-sp3/Dockerfile
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+++ b/mlflow/2.11.1/22.03-lts-sp3/Dockerfile
@@ -0,0 +1,11 @@
+ARG BASE=openeuler/openeuler:22.03-lts-sp3
+FROM ${BASE}
+
+ARG VERSION=2.11.1
+RUN yum install -y python3-pip && yum clean all
+RUN pip3 install mlflow==${VERSION} -i https://pypi.tuna.tsinghua.edu.cn/simple && \
+    groupadd --gid 10001 mlflow && \
+    useradd --uid 10001 --gid mlflow --shell /bin/bash --create-home mlflow
+
+USER 10001
+CMD ["bash"]
diff --git a/mlflow/README.md b/mlflow/README.md
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+++ b/mlflow/README.md
@@ -0,0 +1,30 @@
+# Mlflow
+
+# Quick reference
+
+- The official mlflow docker image.
+
+- Maintained by: [openEuler CloudNative SIG](https://gitee.com/openeuler/cloudnative)
+
+- Where to get help: [openEuler CloudNative SIG](https://gitee.com/openeuler/cloudnative), [openEuler](https://gitee.com/openeuler/community)
+
+# Build reference
+
+1. Build images and push:
+```shell
+docker buildx build -t "openeuler/mlflow:$TAG" --platform linux/amd64,linux/arm64 . --push
+```
+
+We are using `buildx` in here to generate multi-arch images, see more in [Docker Buildx](https://docs.docker.com/buildx/working-with-buildx/)
+
+2. Run:
+```shell
+docker run -it --name mlflow openeuler/mlflow:{TAG}
+```
+
+# Supported tags and respective Dockerfile links
+
+- 2.11.1-oe2203sp3: mlflow v2.11.1, openEuler 22.03-LTS-SP3
+
+## Operating System
+Linux/Unix, ARM64 or x86-64 architecture.
diff --git a/mlflow/doc/image-info.yml b/mlflow/doc/image-info.yml
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+++ b/mlflow/doc/image-info.yml
@@ -0,0 +1,85 @@
+name: Mlflow
+category: ai
+description: MLflow是一个简化机器学习开发的平台，包括跟踪实验、将代码打包成可重复的运行以及共享和部署模型。MLflow提供了一组轻量级API，可与任何现有的机器学习应用程序或库（TensorFlow、PyTorch、XGBoost等）一起使用，无论您当前在何处运行ML代码（例如在笔记本、独立应用程序或云中）。
+environment: |
+  本应用以容器镜像的方式供用户使用，运行于预置docker的Linux环境。Docker的推荐安装方式如下：
+  ## apt安装
+  ### 1.更新系统软件包依赖
+  ```
+  sudo spt update
+  sudo apt install ca-certificates curl gnupg lsb-release
+  ```
+  ### 2.添加Docker官方GPG密钥
+  ```
+  sudo mkdir -p /etc/apt/keyrings
+  curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
+  sudo chmod a+r /etc/apt/keyrings/docker.gpg
+  ```
+  ### 3.添加Docker官方APT源
+  ```
+  echo \"deb [arch=$(dpkg --print-architecture) signed-by=/usr/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable\" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
+  ```
+  ### 4.再次更新软件包索引
+  ```
+  sudo apt update
+  ```
+  ### 5.安装Docker
+  ```
+  sudo apt install docker-ce docker-ce-cli containerd.io
+  ```
+
+  ## yum安装
+  ### 1.安装yum-utils
+  ```
+  sudo yum install -y yum-utils
+  ```
+  ### 2.添加Docker源
+  ```
+  sudo yum-config-manager \
+  > --add-repo \
+  > https://download.docker.com/linux/centos/docker-ce.repo
+  ```
+  ### 3.安装Docker
+  ```
+  sudo yum install -y docker-ce docker-ce-cli containerd.io
+  ```
+
+  注意，在openEuler环境安装docker时，可执行如下命令一键安装
+  ```
+  yum install -y docker
+  ```
+
+  ## 脚本安装
+  ### 1.下载安装脚本
+  ```
+  curl -fsSL https://get.docker.com -o get-docker.sh
+  ```
+  ### 2.使用root权限执行脚本
+  ```
+  sudo sh get-docker.sh
+  ```
+  使用脚本安装方式时，如果用户要指定安装的docker版本，需要自行修改`get-docker.sh`的源码内容。
+
+  ## 验证
+  ```
+  sudo docker run hello-world
+  ```
+
+download: |
+  获取容器镜像
+  ```
+  docker pull openeuler/mlflow:{TAG}
+  ```
+
+install: |
+  启动容器
+  ```
+  docker run -it --name mlflow openeuler/mlflow:{TAG}
+  ```
+    用户可根据自身需求，自定义启动选项。
+
+license: Apache-2.0 license
+similar_packages:
+  - Kubeflow: Kubeflow是一个基于Kubernetes的开源平台，用于在机器学习工作流中管理和部署模型。它提供了用于构建、训练和部署机器学习模型的工具和组件，并支持自动化的部署和扩展。
+dependency:
+  - python3
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diff --git a/mlflow/meta.yml b/mlflow/meta.yml
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+++ b/mlflow/meta.yml
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+2.11.1-oe2203sp3:
+  mlflow/2.11.1/22.03-lts-sp3/Dockerfile
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