Meritshot Tutorials
Flask Tutorial
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Introduction to Flask for Machine LearningIntroduction to Flask for Machine Learning
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Why Use Flask to Deploy ML Models?Why Use Flask to Deploy ML Models?
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Flask vs. Other Deployment Tools (FastAPI, Django, Streamlit)Flask vs. Other Deployment Tools (FastAPI, Django, Streamlit)
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Setting Up the EnvironmentSetting Up the Environment
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Basics of FlaskBasics of Flask
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Flask Application StructureFlask Application Structure
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Running the Development ServerRunning the Development Server
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Debug ModeDebug Mode
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Preparing Machine Learning Models for DeploymentPreparing Machine Learning Models for Deployment
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Saving the Trained ModelSaving the Trained Model
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Loading the Saved Model in PythonLoading the Saved Model in Python
Loading the Saved Model in Python
After saving the trained machine learning model using pickle or joblib, the next step is to load the model into a Python script. This step is crucial for deploying the model in a Flask application or testing its predictions in a new environment.
Why Load a Saved Model?
- Reuse Trained Models: Loading saved models eliminates the need to retrain them.
- Ease of Deployment: Loaded models can be integrated into web apps or APIs.
- Scalability: Enables using the same trained model across multiple applications.
Loading Models Using Different Tools
- Using pickle
To load a model saved with pickle, use the pickle.load() method.
- Using joblib
To load a model saved with joblib, use the joblib.load() method.
Steps to Load the Model
Using pickle
import pickle
# Load the saved model
with open(“house_price_model.pkl”, “rb”) as file:
loaded_model = pickle.load(file)
print(“Model loaded successfully!”)
Using joblib
from joblib import load
# Load the saved model
loaded_model = load(“house_price_model.joblib”)
print(“Model loaded successfully!”)
Testing the Loaded Model
Once the model is loaded, it can be tested using the same sample data or new input data:
# Sample input data
sample_input = np.array([[3.87, 29.0, 6.9841, 1.0238, 3.1400, 37.88, -121.23]])
# Make predictions using the loaded model
sample_prediction = loaded_model.predict(sample_input)
print(f”Predicted House Price: ${sample_prediction[0] * 1000:.2f}”)
Common Issues When Loading Models
- Environment Compatibility: Ensure the same versions of Python and libraries (e.g., scikit-learn, numpy) are used in training and deployment environments.
- File Corruption: Verify the saved model file isn’t corrupted during transfer or storage.
- Missing Dependencies: Install all necessary libraries before loading the model.
Best Practices
- Model Versioning: Clearly label model files with version numbers, e.g., model_v1.pkl.
- Environment Consistency: Use tools like requirements.txt or conda environments to maintain consistent dependencies.
- Secure Storage: Store model files in a secure location accessible to the application.
Frequently Asked Questions
- Q: Can I load a model trained with a different library?
A: No, models saved with pickle or joblib can only be loaded in environments with the same library. Models trained in TensorFlow or PyTorch require their specific saving/loading methods. - Q: What should I do if the model doesn’t load properly?
A: Check for mismatched library versions or corrupted model files. Use a virtual environment with the same dependencies as the training environment. - Q: Can I load the model in a Flask app?
A: Yes, the loaded model can be integrated into Flask routes to serve predictions via APIs. - Q: Is the saved model portable across operating systems?
A: Yes, but ensure the Python and library versions are consistent across platforms. - Q: Can I modify the loaded model?
A: The loaded model is a Python object, so you can modify attributes or retrain it, but it’s better to retrain from scratch if major changes are needed.
