Data Science Dojo delivers data science education, consulting, and technical services to harvest the power of data.

Trademarks: This software listing is packaged by Data Science Dojo. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

About the offer:

Mage AI is a data-pipelining tool, designed to simplify and accelerate the development of machine learning pipelines. With its user-friendly interface and high-level abstractions, Mage AI allows users to focus on defining their machine learning workflows without worrying about the underlying infrastructure or processing logic. It supports a variety of data sources, feature engineering techniques, and model architectures, as well as integrations with popular cloud platforms and data stores. Additionally, Mage AI is constantly evolving and improving, incorporating the latest advancements in machine learning research to provide the most efficient and effective data-pipelining capabilities possible.

Who benefits from this offer:

Data scientists
Machine learning engineers
Developers

What is included in this offer:

Mage-ai is a modern data-pipelining tool that provides various features to simplify and accelerate the data trasformtion. The tool and its pre-requistes essential for its installation covered by us are:

OS: Ubuntu Server 20.04 LTS
Environment: Docker version 23.0.2, build 569dd73
Tool: mage-ai, version 0.8. 41.

and other necessary customization and settings for compatibility.

Technical Specifications:

Minimum memory: 4 GB
Minimum vCPU: 2 vCPUs
Operating System: Operating System: Ubuntu 18.04 LTS or newer, Debian 9 or newer, CentOS 7 or newer, or Fedora 30 or newer.
Storage: 10 GB

Why Mage-ai?

User-Friendly Interface: Mage AI provides a user-friendly interface that makes it easy for users to build machine learning pipelines without requiring advanced technical knowledge.
High-Level Abstractions: Mage AI's high-level abstractions simplify the process of data preparation, feature engineering, model training, and deployment, allowing users to focus on the core of their machine learning projects.
Data Source Flexibility: Mage AI supports a variety of data sources, including local files, databases, and cloud storage, enabling users to work with the data they have and the infrastructure they prefer.
Customizable: Mage AI is highly customizable, allowing users to define their own data processing pipelines and workflows according to their specific needs.
Scalable: Mage AI can scale to handle large datasets and complex machine learning tasks, leveraging distributed computing resources to accelerate performance and improve efficiency.
Integrations: Mage AI integrates with popular cloud platforms and data stores, enabling users to easily connect to their existing infrastructure and data sources.
Support: Mage AI has a dedicated support team and active community of users who provide support and share best practices.

Getting Started:

To access Mage-ai's deployed web interface follow the steps:

SSH into VM(Virtual Machine)
Enter this command: sudo docker start $(sudo docker ps -aqf "status=exited")
Wait for about a minute, then Navigate to http://your_ip:6789

Mage-ai packaged by Data Science DojoData Science Dojo

Mage-ai packaged by Data Science Dojo

Data Science Dojo

Mage-ai packaged by Data Science Dojo

Data Science Dojo

Mage-ai packaged by Data Science Dojo
Data Science Dojo