The 4 main components of AI deployment are:
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Data Preparation: This stage involves ensuring the data used to train the AI model is formatted and processed correctly for deployment. It might involve cleaning the data, handling missing values, and transforming it into a format the model can understand when running in the real world.
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Model Training/Fine-tuning: In some cases, you might need to retrain the AI model on a smaller set of data specific to the deployment environment. This is called fine-tuning and helps the model adapt to the nuances of real-world data.
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Model Deployment and Infrastructure: Here, you choose the computing environment where the AI model will run. This could be on cloud platforms, on-premise servers, or even at the edge (local devices). You'll also need to consider factors like security, scalability, and monitoring during deployment.
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Monitoring and Feedback: Once deployed, it's crucial to monitor the AI model's performance in the real world. This involves tracking metrics like accuracy, bias, and drift (performance changes over time). The feedback from this monitoring can be used to improve the model or retrain it as needed.
