Tips for Building Deployable Models for Predictive Analytics
In order to ensure a successful deployment of the predictive model you’re building, you’ll need to think about deployment very early on. The business stakeholders should have a say in what the final model looks like. Thus, at the beginning of the project, be sure your team discusses the required accuracy of the intended model and how best to interpret its results.
Data modelers should understand the business objectives the model is trying to achieve, and all team members should be familiar with the metrics against which the model will be judged. The idea is to make sure everyone is on the same page, working to achieve the same goals, and using the same metrics to evaluate the benefits of the model.
Keep in mind that the model’s operational environment will most likely be different from the development environment. The differences can be significant, from the hardware and software configurations, to the nature of the data, to the footprint of the model itself. The modelers have to know all the requirements needed for a successful deployment in production before they can build a model that will actually work on the production systems. Implementation constraints can become obstacles that come between the model and its deployment.
Understanding the limitations of your model is also critical to ensuring its success. Pay particular attention to these typical limitations:
- The time the model takes to run
- The data the model needs; sources, types, and volume
- The platform on which the model resides
Ideally, the model has a higher chance of getting deployed when
- It uncovers some patterns within the data that were previously unknown.
- It can be easily interpreted to the business stakeholders.
- The newly uncovered patterns actually make sense businesswise and offer an operational advantage.