3 years ago

Category: Docker, Jenkins, github, automation, artificial-intelligence

It is found that 60–90% of Machine Learning and AI models fail in real-world reason is a lot of manual work for the data scientist to changer values which are known as parameters in the technical world. Here comes the big concept to discuss for automation in AI world i.e. Hyper Parameters whichcan be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process.

This job will start the training of our model, and also predict the accuracy of our model. This is the job that will tweak our model if accuracy is not so good and retrain our model with new parameters like kernel size, batch size, no, of epochs, and also with different no. of CRP and FC layers.

So this is our job6 which will be monitoring our environment every sec on which code is running and the model gets trained.