In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.

The same kind of machine learning model can require different constraints, weights or learning rates to generalize different data patterns. These measures are called hyperparameters, and have to be tuned so that the model can optimally solve the machine learning problem.

So, instead of manually tuning our Hyperparameters, We are giving this job to Jenkins for Automation.

Tools Used-

• Git and Github- for version control and hosting our repository.
• Jenkins- to automate various jobs.
• Rhel8- as a base os for running services like httpd, jenkins,ngrok.
• Docker- to run our python model.

#automation #redhat-linux #jenkins #github #machine-learning