Overview of the ecosystem.Ai Environment

Course Description:

Embark on a high-level exploration of some of ecosystem.Ai’s products. 

ecosystem.Ai offers a unique selection of products and solutions to fit any business prediction need. We have developed the ecosystem.Ai Workbench which is the user interface that allows you access to a multitude of data science and deployment functionalities, and the Client Pulse Responder which is used to make real-time predictions. There are also a series of pre-written Modules that provide predictive solutions to common business problems.

This course is centered around the large degree of functionality available through the ecosystem.Ai Workbench. This product offers easy, consolidated working solutions for any member of your organisation. 

For the business user: discover the simplicity of having a single interface in which to create, manage, monitor and deploy any prediction project. For the data scientist: learn about the vast array of data science functions, and enjoy the ease of data ingestion, viewing, enrichment, exporting and graphing all through one product interface. Lastly, for those in technical implementation: explore the ways in which ecosystem can be used in conjunction with other predictive technologies.

Things to consider before starting this course:

  • If you would like to have access to the ecosystem.Ai Workbench while going through this course (recommended). Navigate to the Ecosystem Workshop and download Docker and the latest version of the Workbench in Getting Started. Once that is done, you can return here to continue learning about the ecosystem.Ai Workbench.
  • If you are interested in how the ecosystem product can enhance business practices, proceed to lesson one of this course: BU Overview.
    • Use the workbench to manage project elements and ensure all members of your teams are working together to propel your business through its own digital transformation.
  • If you are interested in the various data science options ecosystem has to offer, proceed to the second lesson of this course: DS Overview.
    • Explore the data manipulation functionalities available to understand the behaviour of transactions between customer and organisation.
  • For further learning about the many data science options available in the workbench, check out the Feature Engineering course.
  • If you are interested in the various other prediction technologies that ecosystem is compatible with, proceed to the third lesson in this course: TI Overview.
    • Engage the background processes, and manage the flow of information, between open source predictive technologies, github repositories, and monitoring models in production.
  • For further learning about the structure of ecosystem, and compatible technologies, check out the Ecosystem Technology course.

Introducing the ecosystem.Ai Workbench

*Overview video of the ecosystem.Ai Workbench

What is the Workbench?

Companies don’t always have access to the specialized skills needed to solve full-stack prediction problems. It’s expensive to use specialists, get software licensing, and it’s complicated to use multiple technologies to solve problems. 

Introducing the ecosystem.Ai platform. The Workbench platform houses a data layer, modelling workers, and prediction servers. It is central to our unique re-enforcement learning Client Pulse Responder. There are Notebooks which form part of our open-source collaboration with other progressive technologies, and a series of pre-wriiten Modules to assist with predictive solutions to common business problems.

The Workbench is broken down into sections, laid out to reflect the project workflow utilized in most companies. Beginning with Project creation and definition, this section will be continually updated during the course of the project lifecycle. Data and Feature Engineering are the data science work sections. Then there are the Predictor creation and model Deployment sections, which are the creation and testing ground for your models. Then ending back in the Project section for final deployments.

The centralised location, with all of these working elements, allows for a smooth project process, as the journey is funnelled through the single Workbench user interface. With a basic knowledge of data and technical practices, it is possible to organise and engage with the bulk of the predictions process. 

Explore the lessons for each user role and discover the depth of capability available for each.

Further reading
*Links open a new page

What is predictive analytics

Experimentation and Transformation

Understanding the data science project workflow