Overview
Deploying machine learning models is the process of making them available for use in real-world applications. This involves taking a trained model and integrating it into a software environment where it can receive input data, make predictions, and provide results. Understanding deployment is crucial for ensuring that models perform effectively and efficiently in production settings.
📚 Key Learning Objectives
- ✓ Explain the importance of model deployment.
- ✓ Identify different deployment environments for ML models.
- ✓ Describe the steps involved in deploying a model.
- ✓ Evaluate the performance of deployed models.
- ✓ Implement basic deployment strategies for ML applications.
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