Managing Prediction Projects with ecosystem.Ai
The Managing Prediction Projects course will help you learn how to use AI and Machine Learning to implement successful business solutions. This is the most effective way for a Prediction story to thrive is by having a clear understanding of the business problem at hand, and be able to articulate it in such a way that targets predictive capabilities.
Broadly speaking, when using machine learning to solve the problems in your prediction stories, you will need to ensure an understanding of expected outcomes. Be sure to define the problems you are hoping to solve, and attempt to pinpoint the needed data inputs. When evaluating the data, also be sure it’s possible to find reliable patterns and from there select and evaluate your modelling options. Finally, you can begin to interpret and begin to understand the results.
*Figure 1: ecosystem.Ai business value platform
Defining Projects and Prediction Stories
In software development, there is a practice which involves the process of discovering business requirements, and developing solutions, through collaborative efforts. With teams of self-organising, cross-function individuals, customer deliverables can be achieved in an adaptive, fast, and flexible manner. This same principle can be applied to prediction stories, and the ecosystem workbench makes the implementation of this all the more accessible. Responding to change with the aim of adapting to continuously defines the most useful aspect of using agile when working through business problems.
For the learners that are interesting in knowing more about Agile, and the statements that popularised it, read more about the Manifesto for Agile Software Development here.
Identifying Business Problems
Selecting a business case to focus on when constructing projects is not always an easy task. In the midst of a constantly changing landscape, it is very important to maintain variability in identifying opportunities to improve on business practices.
There are a number of working elements to finding a valid business case to build your prediction projects from. First define the problem, then determine your current and future processes in order to evaluate the value potential of fulfilling the prediction story. Finally, and most importantly, outline the details present in your data.
This concept, and others, is further fleshed out in the Prediction Stories course.
*Figure 2: Example of the ecosystem.Ai workbench
The functionalities within the “Projects” section of the ecosystem workbench allow you to build out the details of your hypothesised prediction stories. Upon completion of this course, and a further look at synthesising your prediction stories, you can begin integrating ecosystem.Ai into your business environment.
There are many steps to embarking on a project: from defining your prediction story and ingesting data, to creating feature store predictors. Building and managing these elements from one source – the ecosystem workbench.
Working in almost every department of your company, the ecosystem workbench gives business users a chance to explicate the particulars of the needs of the business project. From there, the data science team can begin to state the data needs for beginning the process of creating models and reading the results. The tech team will also then be well informed about the potential technical needs to ensure the prediction story’s outcome is a success. All of this is possible because the workbench can fit into the overall IT architecture, allowing everyone to participate equally in project observation and management.
Keep your Projects on track and up to date: