A New One-stop AI development and production platform, AlphaIDE
Tags: ai, machinelearning, bigdata, opensource
Published: 2022-06-15 07:00:34

A New One-stop AI development and production platform, [AlphaIDE](https://registry-alphaide.dmetasoul.com/login), quickly deploy an intelligent data platform, providing one-click deployment and operation of Web IDE development interface, data analysis, machine learning training, online prediction, and algorithm experiment application services

I’ve posted about [LakeSoul](https://github.com/meta-soul/LakeSoul), an open-source framework for unified streaming and batch table storage, and [MetaSpore](https://github.com/meta-soul/MetaSpore), an open-source platform for machine learning.

[The design concept of the almighty Opensource project about machine learning platform, MetaSpore](https://dev.to/qazmkop/the-design-concept-of-an-almighty-opensource-project-about-machine-learning-platform-46p)

[The design concept of the best open-source project about big data and data lakehouse](https://dev.to/qazmkop/design-concept-of-a-best-opensource-project-about-big-data-and-data-lakehouse-24o2)

Using LakeSoul and MetaSpore, it is possible to quickly develop real-time data analysis and complete machine learning on offline links, detailed at [Build a real-time machine learning sample library using the best open-source project about big data and data lakehouse, LakeSoul](https://dev.to/qazmkop/build-a-real-time-machine-learning-sample-library-using-the-best-open-source-project-about-big-data-and-data-lakehouse-lakesoul-55f5)

However, there are still many environments to navigate to deploy analytical computing tasks and online services in a production environment. It is necessary to have a simple and user-friendly integrated development environment in the development stage. In the deployment stage, it is necessary to consider the container cluster construction, the high availability scheduling of workflow, the load balancing and elastic scaling of online services, and the adaptation of different public and private cloud platforms. There is a lot of bridging work from business development to deployment. In the past, it required an operation and maintenance team familiar with cloud-native and container technologies to cooperate with development and deployment, which also brought certain communication and time costs.

**Recently, I found a product, [AlphaIDE](https://registry-alphaide.dmetasoul.com/login), that addresses these issues, but it’s still in beta. DMetaSoul officially launched [AlphaIDE](https://registry-alphaide.dmetasoul.com/login) products to solve the above problems, providing complete development and production environment.**Through containerization and seamless connection with mainstream public clouds at home and abroad, an intelligent data platform can be easily and quickly deployed to provide one-click deployment and operation of Web IDE development interface, data analysis, machine learning training, and online prediction and algorithm experiment application services. Since AlphaIDE is in beta, the interface is still Chinese, but it is still worth trying out AlphaIDE with the help of the browser’s translation function. Only a tiny portion of the text on the interface is in Chinese, most of it is in English.

##Key features of AlphaIDE
**1.Data-centric one-stop development and production platform**
AlphaIDE provides complete code development, job scheduling, online production service deployment, and rich Data Ops and ML Ops development tools. Developers can develop and deploy a complete process from data ingesting to model computation to online experimentation by focusing on their data and model computation processes without worrying about the underlying infrastructure. AlphaIDE helps developers mask details such as the underlying operations of the cluster and the docking of cloud vendors, allowing data scientists and algorithm engineers to develop and go online independently.

**2.Integrate standard development components**
AlphaIDE integrates the self-developed frameworks of LakeSoul and MetaSpore and provides open-source computing frameworks such as Spark and Flink. AlphaIDE integrates custom Kubeflow, supports Notebook’s workflow production scheduling, and includes custom Jupyter/CodeServer container images without requiring developers to package their runtime images.

**3.Cloud-native private deployment**
AlphaIDE can support private deployment under a user’s cloud service account. The user only needs to provide the authorization of one cloud account, which can be canceled after creating the cluster. Users can deploy AlphaIDE automatically under their cloud account without manual intervention. The resources and data of the entire cluster are stored in the user’s cloud VPCS (virtual subnets), which can connect to the existing databases and storage systems on the Intranet without repeated remote data import, ensuring data privacy and security.

**4.Rich enterprise-level functionality**
AlphaIDE provides SSO and Role-Based Access Control (RBAC) permission management, connecting with the enterprise’s account system, such as LDAP, or the third-party account system, such as Github Team. AlphaIDE also supports resource quota control and integrates log collection queries, service status, performance monitoring, etc.

##The trial AlphaIDE
AlphaIDE Demo service is available online. Users can enter the Demo cluster after login and registration to experience the core functions of AlphaIDE for free. There is no English version of the product interface yet, but it is still worth trying out AlphaIDE with the help of the browser’s translation function.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/esgny9odp8exi4zh9ip9.png)

Users can use the https://registry-alphaide.dmetasoul.com/ to register the demo cluster using an email account. After passing the email verification, click to enter the free test cluster and log in to the Demo cluster using the email address and password specified during registration. For details, see [AlphaIDE Service Usage Guide](https://dev.to/qazmkop/usage-guidequickly-deploy-an-intelligent-data-platform-with-the-one-stop-ai-development-and-production-platform-alphaide-4bgn)
In addition to the basic IDE instructions, AlphaIDE also provides code demos to run tests.

Run LakeSoul flow batch of one of the samples: https://github.com/meta-soul/LakeSoul/wiki/Lakesoul-IDE-Demo

Run MetaSpore model training example: https://github.com/meta-soul/MetaSpore/blob/main/tutorials/metaspore-getting-started.ipynb

Run a Demo of DMetaSoul previously released MovieLens end-to-end recommendation system: https://github.com/meta-soul/MetaSpore/tree/main/demo/movielens/offline#readme

Welcome you to sign up and try AlphaIDE.