Your algorithms are underperforming. What can you do to get the most reliable artificial intelligence tools?

1 Evaluate your data

The quality of your data is crucial for the success of your algorithms. You need to make sure that your data is relevant, clean, diverse, and representative of your problem domain. You also need to avoid overfitting, underfitting, and data leakage, which can affect your algorithm's generalization and validation. To evaluate your data, you can use techniques such as exploratory data analysis, data preprocessing, feature engineering, feature selection, and cross-validation.

4 Debug your algorithm

Even if you have optimized your algorithm, you may still encounter errors, bugs, or anomalies that can affect your algorithm's functionality and reliability. You need to identify, diagnose, and fix these issues as soon as possible to prevent them from escalating or propagating. You also need to verify and validate your algorithm's output and behavior against your expectations and requirements. To debug your algorithm, you can use techniques such as logging, tracing, profiling, and testing.

5 Update your algorithm

Your algorithm is not a static entity that can remain unchanged forever. You need to update your algorithm regularly to keep up with the changes in your data, environment, and demand. You also need to improve your algorithm continuously to incorporate new features, feedback, and innovations. To update your algorithm, you can use strategies such as incremental learning, transfer learning, active learning, and reinforcement learning.

6 Document your algorithm

Your algorithm is not only a technical product, but also a communication tool. You need to document your algorithm clearly and comprehensively to explain its purpose, design, implementation, and performance. You also need to document your algorithm ethically and transparently to justify its decisions, assumptions, and limitations. To document your algorithm, you can use formats such as comments, docstrings, notebooks, and reports.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?