Avoiding Heartburn in Machine Learning: Key Considerations Before Developing a Full-Fledged Solution

Ensuring Success by Asking Key Questions Before Developing ML/DL Solutions

Machine learning models have the potential to efficiently tackle intricate business problems, ranging from identifying fraudulent transactions or predicting demand using time-series data to utilizing conversational AI like chatGPT or identifying anomalous parts using computer vision. These models undergo rigorous training and testing on datasets, and once their performance metrics are satisfactory on new data, they are deemed production-ready.

However, before embarking on a full-fledged machine learning solution, it is essential to ask critical questions to avoid potential challenges down the line.

It is essential first to determine if machine learning or deep learning is the right solution for the problem and that it is not an over-engineered solution for an issue that could be solved using more straightforward tools or techniques.

Before starting a machine learning or deep learning project for production deployment, what are the key factors to consider?

The factors to increase the likelihood of building an effective and robust machine learning solutions in a production environment that meet business requirements and deliver meaningful results are

• Defining SMART success criteria