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The concept of MLOps is still relatively young, but every day it becomes more and more in demand. In practice, the problem of introducing ML models into a real business is not limited to data preparation, development and training of a neural network or other Machine Learning algorithm. The quality of a production solution is influenced by many factors, from verification of datasets to testing and deployment in a production environment in the form of a reliable Big Data application. The students will be familiarized with practices for integrated and automated lifecycle management of machine learning systems, combining their development and operational support operations, incl. integration, testing, release, deployment and infrastructure management.