Define clear objectives and metrics

There’s a hidden costs to AI development that no one is talking about. This culprit is benchmarks.

Employing evaluation benchmarks in your model before deployment is critical to the success of any project.

Not knowing how your model is performing means there is no hope of improving its predictions or knowing when to retrain with new data. Those involved in deployment need to be critical when ensuring that the proper objectives are clearly defined. Aligned with performance benchmarks.

These measurable objectives should simultaneously serve the businesses growth and the experience the customer has using your product.

Adopt a modular and agile approach

The single key to success in scaling an AI project is that you have to go slow. Not what people expect.

Start by clearly defining what you want to get accomplished. You cannot set a clear deadline for release without the proper expectation of what will be completed by the set date.

In order to do this a modular approach must be taken. Defining clear boundaries for segments of work. Grouped together by shared or common functionality.

It’s common that you cannot deploy a flawless project from the start so an iterative approach to development must be taken. Deployments need to be monitored to find bugs or bad user experiences.

Small units of work are then cyclicly completed to ensure that every small detail is eventually addressed.

Monitor and optimize model performance

The REAL reason any AI project is successful at scale is the data.

There is a common saying in AI, “garbage in. garbage out.” This states that the quality of an AI’s performance is dependent on the quality of the information that is fed to it.

As a project scales and starts to serve more users this only becomes more important. While ata needs to be secure and privacy policies need to be in compliance focus should also be on “data drift”.

Data drift is the concept that while the initial training set (data) gets you the results you want for your model the data set you own changes. And if the new set of data is different from the old set then the model will behave differently.

This is the primary reason monitoring is very important.