Transforming AI Prototypes into Production Solutions

The Production Challenge

AI prototypes often work perfectly in controlled environments, but fail spectacularly when faced with real data and production loads. In this article, we explore best practices for transforming prototypes into robust solutions.

"The difference between a prototype and a production solution is not in the complexity of the algorithm, but in the robustness of the implementation."

1. Data Validation

The first step is to implement robust validations for input data. Prototypes often assume that data will always be correct, but in production this is rarely true.

Additionally, it's important to consider the data model being used, as it can be a significant factor in the robustness of the solution. If the data model is not clear, the information being generated can be incorrect, incomplete, or difficult to access. This can lead to performance, security, and maintenance issues.

2. Monitoring and Logging

Implementing a comprehensive monitoring system is crucial for detecting problems before they affect users.

Usually, AI prototypes don't have a comprehensive monitoring system, which makes it difficult to detect problems and correct them in time. The most common forms of monitoring are:

• Informational logs: that notify about the result of some action or event that occurred in the system.
• Error logs: that notify about errors that occurred in the system.
• Activity logs: that notify about actions that the user performs in the system and are particularly useful for investigating problems.
• Error reporting: where errors that occurred in the system are recorded and all necessary information for their correction is provided.

3. Automated Testing

Automated tests are the foundation of any reliable production system, there are different types:

• Unit tests: that test a unit of code in isolation.
• Integration tests: that test the interaction between different units of code.
• End-to-end tests: that test complete application flows.

Tests are a powerful tool to guarantee software quality and ensure that the system's behavior is as expected. If you're thinking about putting a system into production, make sure you have a robust battery of tests.