The AI Model Lifecycle consists of six key stages: data collection (gathering raw data), data cleaning (preparing and cleaning the data), model selection (choosing the appropriate algorithm), model training and testing (training the model and evaluating its performance), deployment (implementing the model in production), and monitoring (continuously tracking model performance after deployment).
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So, first, data collection.
data set responsible unbiased process, cleaning So, in the right, multiple steps are there. Then, comes the model selection.
model the model train model test model Then, correct the work out of that. work out So, a back-end system other post model Then, you can deploy model. Then, monitoring model So, in the right, there are multiple steps AI life cycle. So, understand look number helpful work In the right, useful topics are number YouTube channel subscribe and you go there.
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