Requirements for ML Systems

Framing ML Problems

1. Reliability
2. Scalability
3. Maintainability
4. Adaptibility

One day, your boss hears about a rival bank that uses ML to speed up their customer service support that supposedly helps the rival bank process their customer requests two times faster. He orders your team to look into using ML to speed up your customer service support too.

Slow customer support is a problem, but it’s not an ML problem. An ML problem is defined by inputs, outputs, and the objective function that guides the learning process—none of these three components are obvious from your boss’s request. It’s your job, as a seasoned ML engineer, to use your knowledge of what problems ML can solve to frame this request as an ML problem.

Upon investigation, you discover that the bottleneck in responding to customer requests lies in routing customer requests to the right department among four departments: accounting, inventory, HR (human resources), and IT. You can alleviate this bottleneck by developing an ML model to predict which of these four departments a request should go to. This makes it a classification problem. The input is the customer request. The output is the department the request should go to. The objective function is to minimize the difference between the predicted department and the actual department.

Mutlple ways to frame a problem

Problem: Predict the app a user will most likely open next

• If we are using classification, every time a new app comes in, we need to retrain the model.
• If we are using regression, we only need to input app features as the input and the model remain the same.