AI

8 Tips for Getting Your AI Model Production Ready

A talk at this year’s O’Reilly AI Conference explored how you can turn AI research into a revenue engine, with a focus on the challenges of putting an ML model into production. Many of these challenges occur because of a lack of preparation, meaning they can be significantly mitigated and even avoided by making sure your AI model is production-ready.

Here are our top 8 tips to help you do just that.

1. Make your model robust

Making your model robust is important for any project, but it’s absolutely crucial if you’re expecting it to go into production. At this stage, the model receives real (and therefore new) data, so it needs to be able to consistently perform well as this data changes.

By tightening up your AI model before production, you’ll avoid an inconsistent or unpredictable performance.

2. Monitor performance

For the same reason as the previous tip, you need to continually monitor how your model is performing. That means observing how changes in the input data are reflected in the model’s performance.

Invest some time doing this beforehand, and keep up consistent monitoring once your model is in production.

3. Define a specific target variable

Without a well-defined target variable, the outcome of your AI model becomes meaningless. Since this is the main question your AI model has been designed to answer in the first place, the more specific you can make the target variable the more your model will learn from the dataset.

4. Understand your resources

Before putting your model into production, make sure you understand what resources will be available in the home production infrastructure. This is important because it ensures you understand any limitations you might have up-front, as well as seeing the end to and flow of the model and the ways it can be exploited.

5. Consider your model inputs

It’s a good idea to think about any requirements from a data input perspective from the offset. For example, will the model need to perform any data transformation before being able to ingest it? Understanding these considerations before production will help you avoid running into issues.

6. Consider your model outputs

Knowing up-front what output conditions you’ll need from your model will save you time later on. Think about how it will be exploited in production.

For example, will the output simply be saved in a database, or will you need any visual graphs or alert triggers?

7. Align all parties

For any AI project to be successful, there needs to be a close relationship between all parties involved. This includes the business/client, the data scientists, and the infrastructure/engineering team.

Getting everyone on the same page and making sure the objectives are clear will ensure better communication and help the project run smoothly throughout.

8. Make sure the output can be managed

Though it might sound silly, many companies don’t consider what they’ll do with the output of a project before putting it into production. That is, they don’t have a plan in place for how to manage what the project produces. You can avoid this issue by making sure this is established and understood at the offset.

It can be really tempting to push an exciting project into production before its ready. By taking the time to prepare your model and consider the wider project implications before this stage, you’ll save time, avoid obstacles and get a much better performance from your AI solution.

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