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 production environment where it can receive data and provide predictions. Understanding deployment is crucial for ensuring that models work effectively and efficiently outside of the lab setting. This topic covers the basics of deployment strategies, tools, and best practices.
📚 Key Learning Objectives
- ✓ Explain the importance of deploying ML models.
- ✓ Identify common deployment strategies for ML models.
- ✓ Describe tools used for model deployment.
- ✓ Implement a simple ML model deployment.
- ✓ Evaluate the performance of deployed models.
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