ML model deployment is the process of making your machine learning model available in a production environment, where it can provide predictions to other software systems. It involves integrating the model into an existing production environment that can provide the model with the input data it requires and can utilize its predictions.
The model deployment is the last stage in the machine learning lifecycle, and it is critical because a model can only deliver value if it is successfully deployed in the production.
ML Model Deployment in practice
ML model deployment works through the following key steps:
- Model training: This is the initial step where the model is trained with the available data and the suitable algorithm for the specific use case. This process involves tweaking and testing the model until it performs at the optimum level.
- Model Testing: Once the model is trained, it is essential to test the model using new unseen data to evaluate its performance and generalization abilities. This prevents overfitting and ensures the model can handle new data.
- Model Conversion: In this stage, the model is converted into a format that can be used in the production environment. This could be a binary format, a specific file type, or a format such as ONNX (Open Neural Network Exchange), which is suitable for various platforms.
- Integration: Here, the model is integrated with the production environment. This environment could be a software application, a website, or any system where the model's predictions are needed. The model can be hosted locally or on cloud platforms like AWS, Google Cloud, or Azure.
- Monitoring and updating: After the model is deployed, it needs to be continuously monitored to ensure it is making accurate predictions. If the model's accuracy starts decreasing, it might need to be retrained with new data or tweaked to maintain its performance.
The exact method of deployment can vary greatly depending on the specific situation and the technology stack of the production environment. Some deployment options include REST APIs, direct integration into an application via a library, or deployment on a cloud service.
In conclusion, ML model deployment allows businesses to use their machine learning models to serve predictions in real-time and automatically learn from the feedback to improve future predictions.
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