Preparing a scoring script is a crucial step in deploying the machine learning model. The scoring script is a standalone script (or application) that loads the trained ML model, performs any necessary preprocessing on new input data, runs this data through the model to get predictions, and then outputs these predictions.
It is the interface between the saved model and the outside world, allowing other systems to use the model to make predictions on new, unseen data.
Here’s a step-by-step breakdown:
- Load the Model:
- At the start of the script, there should be a code to load the previously serialized (saved) model into memory. This involves deserializing the model file that has been saved after training the model.
- import joblib model = joblib.load(‘model.pkl’)
- Preprocess Input Data:
- When the scoring script receives new input data (e.g., via an API request), this data will likely need to be preprocessed before it can be fed into the model. This preprocessing needs to be identical to the preprocessing that was applied to the training data.
- This might involve cleaning the data, handling missing values, encoding categorical variables, scaling numerical features, etc.
- Define a Prediction Function:
- Define a function in the script that takes the preprocessed input data, runs it through the loaded model, and returns the model’s predictions.
- Exposing the Prediction Function:
- To enable other systems to use the model, expose the prediction function through an API endpoint. This could be done using web frameworks like Flask, FastAPI, Django, etc.
- Error Handling and Logging:
- Add error handling to the script so that it fails gracefully if something goes wrong
- Also, add logging to the script so that you can debug issues and keep track of requests and predictions.
- Testing:
- Before deploying the script, it should be thoroughly tested, ideally in an environment that mimics the actual production system as closely as possible.
The scoring script is deployed alongside the model, and when new data comes in (e.g., a user on a website wants a prediction), the data is sent to the scoring script, which preprocesses the data, runs it through the model, and sends back the prediction.