The Bottom Line:
- Projects as repositories for version control
- Process automation with scheduler
- Creating web service endpoints for data sharing
- Visualization with Panopticon and Grafana dashboards
- Data science environment with RapidMiner Notebooks
Introduction to AI Hub Platform Components
Overview of AI Hub Platform Components
As you explore the AI Hub platform, you will encounter essential components such as projects, processes, and services. These elements play a crucial role in managing your data science tasks efficiently.
Working with Projects on AI Hub
Projects on AI Hub are structured as versioned folders, utilizing the open-source G system for effective version control. These projects serve as comprehensive repositories that encompass various items like data models and other project-specific objects. Access control is applied at the project level, ensuring that collaborators can view all contents within a shared project.
Execution and Scheduling Capabilities
Within the AI Hub, you have the capability to manage process execution through executions and schedules. The scheduler allows you to automate process execution based on defined schedules, offering options for daily, hourly, or weekly runs. Job agents facilitate parallel job executions and ensure efficient workflow management.
Working with Projects and Version Control System
Project Management and Version Control
Projects on AI Hub are structured as versioned folders with robust version control capabilities using the popular open-source Git system. These projects act as comprehensive repositories containing various elements such as data models and project-specific objects. Access control is at the project level, ensuring complete visibility for shared projects among collaborators.
Process Execution and Automation
With executions and schedules in the AI Hub platform, you can efficiently manage process execution. The scheduler allows for automated process runs based on predefined schedules, offering flexibility in setting up daily, hourly, or weekly executions. Job agents enable smooth parallel job execution, enhancing workflow efficiency.
Managing Process Executions and Schedules
Managing Process Executions and Schedules
Utilize the executions and schedules feature within the AI Hub platform to oversee process executions effectively. The scheduler empowers you to automate process runs according to specified schedules, offering the flexibility to set up recurring tasks on a daily, hourly, or weekly basis. Job agents are also available to manage parallel job executions, ensuring smooth workflow coordination.
Utilizing Endpoints for Web Services
Exploring Web Service Endpoints
Endpoints in AI Hub serve as a modern and advanced method for exposing RapidMiner processes as web services, also known as REST API endpoints. This standardized approach facilitates seamless data sharing across different environments and applications.
Configuring and Managing Endpoints Interface
The endpoints interface allows you to configure endpoints graphically by selecting processes, naming the endpoint, and specifying parameters for process behavior customization. Based on a scalable architecture, endpoints can be distributed across multiple servers while being centrally managed.
Utilizing Panopticon Visualization
Panopticon serves as the standard visualization environment in alter.ai, offering a range of powerful visualization options from standard to advanced chart types. With data sources including databases and CSV files, the primary method of accessing data is through AI Hub endpoints, facilitating efficient dashboard creation.
Exploring Visualization Options with Panopticon and Grafana
Exploring Data Visualization with Panopticon and Grafana
Discover the powerful visualization capabilities offered by Panopticon within alter.ai. From simple standard charts to advanced visualizations, Panopticon provides a versatile environment for data representation. Easily access data from various sources, with a focus on utilizing AI Hub endpoints for streamlined dashboard development.
Creating Dashboards with Grafana Integration
Leverage the integration of Grafana into the AI Hub platform to build interactive and dynamic dashboards. Based on the open-source Grafana project, this feature allows for easy dashboard creation through drag-and-drop functionality, enabling customization with filters and interaction methods. Ideal for time series data, Grafana offers a range of chart options, complementing Panopticon’s capabilities for specialized chart types.
Enhancing Data Science Workflow with RapidMiner Notebooks
Explore the collaborative and code-based development environment provided by RapidMiner Notebooks within the AI Hub platform. Integrated with other components of the platform, these notebooks allow for a seamless transition between documentation and code execution. Utilize customizable Python environments and connect with Studio administrators for enhanced coding control and execution capabilities.
