ML lifecycle phase - ML problem
framing
In this phase, the business problem is framed as a machine learning problem: what is observed and what should be predicted (known as a label or target variable). Determining what to predict and how performance must be optimized is a key step in ML. For example, consider a scenario where a manufacturing company wants to maximize profits. There are several possible approaches including forecasting sales demand for existing product lines to optimize output, forecasting the required input materials and components required to reduce capital locked up in stock, and predicting sales for new products to prioritize new product development.
It's necessary to work through framing the ML problems in line with the business challenge.
Steps in this phase:
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Define criteria for a successful outcome of the project.
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Establish an observable and quantifiable performance metric for the project, such as accuracy.
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Define the relationship between the technical metric (for example, accuracy) and the business outcome (for example, sales).
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Help ensure business stakeholders understand and agree with the defined performance metrics.
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Formulate the ML question in terms of inputs, desired outputs, and the performance metric to be optimized.
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Evaluate whether ML is the right approach. Some business problems don’t need ML as simple business rules can do a much better job. For other business problems, there might not be sufficient data to apply ML as a solution.
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Create a strategy to achieve the data sourcing and data annotation objective.
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Start with a simple model that is easy to interpret, and makes debugging more manageable.
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Map the technical outcome to a business outcome.
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Iterate on the model by gathering more data, optimizing the parameters, or increasing the complexity as needed to achieve the business outcome.
Best practices
